Search

Abstract In the last three decades, the Steered Response Power (SRP) method has been widely used for the task of Sound Source Localization (SSL), due to its satisfactory localization performance on moderately reverberant and noisy scenarios. Many works have analysed and extended the original SRP method to reduce its computational cost, to allow it to locate multiple sources, or to improve its performance in adverse environments. In this work, we review over 200 papers on the SRP method and its variants, with emphasis on the SRP-PHAT method. We also present eXtensible-SRP, or X-SRP, a generalized and modularized version of the SRP algorithm which allows the reviewed extensions to be implemented. We provide a Python implementation of the algorithm which includes selected extensions from the literature.

Abstract Machine learning and neural networks have advanced numerous research domains, but challenges such as large training data requirements and inconsistent model performance hinder their application in certain scientific problems. To overcome these challenges, researchers have investigated integrating physics principles into machine learning models, mainly through (i) physics-guided loss functions, generally termed as physics-informed neural networks, and (ii) physics-guided architectural design. While both approaches have demonstrated success across multiple scientific disciplines, they have limitations including being trapped to a local minimum, poor interpretability, and restricted generalizability beyond sampled data range. This paper proposes a new physics-informed neural network (PINN) architecture that combines the strengths of both approaches by embedding the fundamental solution of the wave equation into the network architecture, enabling the learned model to strictly satisfy the wave equation. The proposed point neuron learning method can model an arbitrary sound field based on microphone observations without any dataset. Compared to other PINN methods, our approach directly processes complex numbers, offers better interpretability, and can be generalized to out-of-sample scenarios. We evaluate the versatility of the proposed architecture by a sound field reconstruction problem in a reverberant environment. Results indicate that the point neuron method outperforms two competing methods and can efficiently handle noisy environments with sparse microphone observations.

Abstract Synthesis artifacts that span scales from small to large are important cues for spoofing detection. However, few spoofing detection models leverage artifacts across different scales together. In this paper, we propose a spoofing detection system built on SincNet and Deep Layer Aggregation (DLA), which leverages speech representations at different levels to distinguish synthetic speech. DLA is totally convolutional with an iterative tree-like structure. The unique topology of DLA makes possible compounding of speech features from convolution layers at different depths, and therefore the local and the global speech representations can be incorporated simultaneously. Moreover, SincNet is employed as the frontend feature extractor to circumvent manual feature extraction and selection. SincNet can learn fine-grained features directly from the input speech waveform, thus making the proposed spoofing detection system end-to-end. The proposed system outperforms the baselines when tested on ASVspoof LA and DF datasets. Notably, our single model surpasses all competing systems in ASVspoof DF competition with an equal error rate (EER) of 13.99%, which demonstrates the importance of multi-scale information aggregation for synthetic speech detection.

Abstract The degree of correlation between the sounds received by the ears significantly influences the spatial perception of a sound image. Audio signal decorrelation is, therefore, a commonly used tool in various spatial audio rendering applications. In this paper, we propose a multi-channel extension of a previously proposed decorrelation method based on generative adversarial networks. A separate generator network is employed for each output channel. All generator networks are optimized jointly to obtain a multi-channel output signal with the desired properties. The training objective includes a number of individual loss terms to control both the input-output and the inter-channel correlation as well as the quality of the individual output channels. The proposed approach is trained on music signals and evaluated both objectively and through formal listening tests. Thereby, a comparison with two classical signal processing-based multi-channel decorrelators is performed. Additionally, the influence of the number of output channels, the individual loss term weightings, and the employed training data on the proposed method’s performance is investigated.

Abstract Self-supervised learning for speech pre-training models has achieved remarkable success in acquiring superior speech contextual representations by learning from unlabeled audio, excelling in numerous downstream speech tasks. However, the pre-training of these models necessitates significant computational resources and training duration, presenting a high barrier to entry into the realm of pre-training learning. In our efforts, by amalgamating the resource-efficient benefits of the generative learning model, Masked Auto Encoder, with the efficacy of the vector quantization method in discriminative learning, we introduce a novel pre-training framework: Speech Vector Quantization Masked Auto Encoder (SVQ-MAE). Distinct from the majority of SSL frameworks, which require simultaneous construction of speech contextual representations and mask reconstruction within an encoder-only module, we have exclusively designed a decoupled decoder for pre-training SVQ-MAE. This allows the additional decoupled decoder to undertake the mask reconstruction task solely, reducing the learning complexity of pretext tasks and enhancing the encoder’s efficiency in extracting speech contextual representations. Owing to this innovation, by using only 4 GPUs, SVQ-NAE can achieve high performance comparable to wav2vec 2.0, which requires 64 GPUs for training. In the Speech Processing Universal Performance Benchmark, SVQ-MAE surpasses wav2vec 2.0 in both keyword spotting and emotion recognition tasks. Furthermore, in cross-lingual ASR for Mandarin, upon fine-tuning on AISHELL-1, SVQ-MAE achieves a Character Error Rate of 4.09%, outperforming all supervised ASR models.

Abstract Binaural audio recordings become increasingly popular in multimedia repositories, posing new challenges in indexing, searching, and retrieval of such excerpts in terms of their spatial audio scene characteristics. This paper presents a new method for the automatic estimation of one of the most important spatial attributes of binaural recordings of music, namely “ensemble width.” The method has been developed using a repository of 23,040 binaural excerpts synthesized by convolving 192 multi-track music recordings with 30 sets of head-related transfer functions (HRTF). The synthesized excerpts represented various spatial distributions of music sound sources along a frontal semicircle in the horizontal plane. A binaural auditory model was exploited to derive the standard binaural cues from the synthesized excerpts, yielding a dataset representing interaural level and time differences, complemented by interaural cross-correlation coefficients. Subsequently, a regression method, based on gradient-boosted decision trees, was applied to the formerly calculated dataset to estimate ensemble width values. According to the obtained results, the mean absolute error of the ensemble width estimation averaged across experimental conditions amounts to 6.63° (SD 0.12°). The accuracy of the method is the highest for the recordings with ensembles narrower than 30°, yielding the mean absolute error ranging between 0.8° and 10.2°. The performance of the proposed algorithm is relatively uniform regardless of the horizontal position of an ensemble. However, its accuracy deteriorates for wider ensembles, with the error reaching 25.2° for the music ensembles spanning 90°. The developed method exhibits satisfactory generalization properties when evaluated both under music-independent and HRTF-independent conditions. The proposed method outperforms the technique based on “spatiograms” recently introduced in the literature.

Abstract Over the past decade, the prevalence of neurological diseases has significantly risen due to population growth and aging. Individuals suffering from spastic paralysis, brain attack, and idiopathic Parkinson’s disease (PD), among other neurological illnesses, commonly suffer from dysarthria. Early detection and treatment of dysarthria in these patients are essential for effectively managing the progression of their disease. This paper provides UTrans-DSR, a novel encoder-decoder architecture for analyzing Mel-spectrograms (generated from audios) and classifying speech as healthy or dysarthric. Our model employs transformer encoder features based on a hybrid design, which includes the feature enhancement block (FEB) and the vision transformer (ViT) encoders. This combination effectively extracts global and local pixel information regarding localization while optimizing the mel-spectrograms feature extraction process. We keep up with the original class-token grouping sequence in the vision transformer while generating a new equivalent expanding route. More specifically, two unique growing pathways use a deep-supervision approach to increase spatial data recovery and expedite model convergence. We add consecutive residual connections to the system to reduce feature loss while increasing spatial data retrieval. Our technique is based on identifying gaps in mel-spectrograms distinguishing between normal and dysarthric speech. We conducted several experiments on UTrans-DSR using the UA speech and TORGO datasets, and it outperformed the existing top models. The model performed significantly in pixel’s localized and spatial feature extraction, effectively detecting and classifying spectral gaps. The Tran-DSR model outperforms previous research models, achieving an accuracy of 97.75%.

Abstract This paper proposes novel methods for extracting a single Speech signal of Interest (SOI) from a multichannel observed signal in underdetermined situations, i.e., when the observed signal contains more speech signals than microphones. It focuses on extracting the SOI using prior knowledge of the SOI’s Direction of Arrival (DOA). Conventional beamformers (BFs) and Blind Source Separation (BSS) with spatial regularization struggle to suppress interference speech signals in such situations. Although Switching Minimum Power Distortionless Response BF (Sw-MPDR) can handle underdetermined situations using a switching mechanism, its estimation accuracy significantly decreases when it relies on a steering vector determined by the SOI’s DOA. Spatially-Regularized Independent Vector Extraction (SRIVE) can robustly enhance the SOI based solely on its DOA using spatial regularization, but its performance degrades in underdetermined situations. This paper extends these conventional methods to overcome their limitations. First, we introduce a time-varying Gaussian (TVG) source model to Sw-MPDR to effectively enhance the SOI based solely on the DOA. Second, we introduce the switching mechanism to SRIVE to improve its speech enhancement performance in underdetermined situations. These two proposed methods are called Switching weighted MPDR (Sw-wMPDR) and Switching SRIVE (Sw-SRIVE). We experimentally demonstrate that both surpass conventional methods in enhancing the SOI using the DOA in underdetermined situations.

Abstract Over the past few decades, extensive research has been devoted to the design of artificial reverberation algorithms aimed at emulating the room acoustics of physical environments. Despite significant advancements, automatic parameter tuning of delay-network models remains an open challenge. We introduce a novel method for finding the parameters of a feedback delay network (FDN) such that its output renders target attributes of a measured room impulse response. The proposed approach involves the implementation of a differentiable FDN with trainable delay lines, which, for the first time, allows us to simultaneously learn each and every delay-network parameter via backpropagation. The iterative optimization process seeks to minimize a perceptually motivated time-domain loss function incorporating differentiable terms accounting for energy decay and echo density. Through experimental validation, we show that the proposed method yields time-invariant frequency-independent FDNs capable of closely matching the desired acoustical characteristics and outperforms existing methods based on genetic algorithms and analytical FDN design.

Abstract This paper addresses the challenge of online blind speaker separation in a multi-microphone setting. The linearly constrained minimum variance (LCMV) beamformer is selected as the backbone of the separation algorithm due to its distortionless response and capacity to create a null towards interfering sources. A specific instance of the LCMV beamformer that considers acoustic propagation is implemented. In this variant, the relative transfer functions (RTFs) associated with each speaker of interest are utilized as the steering vectors of the beamformer. A control mechanism is devised to ensure robust estimation of the beamformer’s building blocks, comprising speaker activity detectors and direction of arrival (DOA) estimation branches. This control mechanism is implemented as a multi-task deep neural network (DNN). The primary task classifies each time frame based on speaker activity: no active speaker, single active speaker, or multiple active speakers. The secondary task is DOA estimation. It is implemented as a classification task, executed only for frames classified as single-speaker frames by the primary branch. The direction of the active speaker is classified into one of the multiple ranges of angles. These frames are also leveraged to estimate the RTFs using subspace estimation methods. A library of RTFs associated with these DOA ranges is then constructed, facilitating rapid acquisition of new speakers and efficient tracking of existing speakers. The proposed scheme is evaluated in both simulated and real-life recordings, encompassing static and dynamic scenarios. The benefits of the multi-task approach are showcased, and significant improvements are evident, even when the control mechanism is trained with simulated data and tested with real-life data. A comparison between the proposed scheme and the independent low-rank matrix analysis (ILRMA) algorithm reveals significant improvements in static scenarios. Furthermore, the tracking capabilities of the proposed scheme are highlighted in dynamic scenarios.

Abstract The sound produced by vehicles driving on roadways constitutes one of the dominant noise sources in urban areas. The impact of traffic noise on human activities and the related investigation on modeling, assessment, and abatement strategies fueled the research on the simulation of the sound produced by individual passing vehicles. Simulators enable in fact to promote a perceptual assessment of the nature of traffic noise and of the impact of single road agents on the overall soundscape. In this work, we present TrafficSoundSim, an open-source framework for the acoustic simulation of vehicles transiting on a road. We first discuss the generation of the sound signal produced by a vehicle, represented as a combination of road/tire interaction noise and engine noise. We then introduce a propagation model based on the use of variable length delay lines, allowing to simulate acoustic propagation and Doppler effect. The proposed simulator incorporates the effect of air absorption and ground reflection, modeled via complex-valued reflection coefficients dependent on the road surface impedance, as well as a model of the directivity of sound sources representing the passing vehicles. The source signal generation and the propagation stages are decoupled, and all effects are implemented using finite impulse response filters. Moreover, no recorded data is required to run the simulation, making the framework flexible and independent on data availability. Finally, to validate the framework capability to accurately simulate passing vehicles, a comparison between synthetic and recorded pass-by events is presented. The validation shows that sounds generated with the proposed method achieve a good match with recorded events in terms of power spectral density and psychoacoustics metrics as well as a perceptually plausible result.

Abstract With the rapid development of social networks, short videos have become a popular form of content, especially dance videos. In this context, research on automatically generating music for dance videos shows significant practical value. However, existing studies face challenges such as limited richness in music timbre and lack of synchronization with dance movements. In this paper, we propose Dance2Music-Diffusion, a novel framework for music generation from dance videos using latent diffusion models. Our approach includes a motion encoder module for extracting motion features and a music diffusion generation module for generating latent music representations. By integrating dance type monitoring and latent diffusion techniques, our framework outperforms existing methods in generating complex and rich dance music. We conducted objective and subjective evaluations of the results produced by various existing models on the AIST++ dataset. Our framework shows outstanding performance in terms of beat recall rate, consistency with GT beats, and coordination with dance movements. This work represents the state of the art in automatic music generation from dance videos, is easy to train, and has implications for enhancing entertainment experiences and inspiring innovative dance productions. Sample videos of our generated music and dance can be viewed at https://youtu.be/eCvLdLdkX-Y . The code is available at https://github.com/hellto/dance2music-diffusion .

Abstract Multi-channel speech enhancement plays a critical role in numerous speech-related applications. Several previous works explicitly utilize deep neural networks (DNNs) to exploit tempo-spectral signal characteristics, which often leads to excellent performance. In this work, we present a time-frequency fusion model, namely TFFM, for multi-channel speech enhancement. We utilize three cascaded U-Nets to capture three types of high-resolution features, aiming to investigate their individual contributions. To be specific, the first U-Net keeps the time dimension and performs feature extraction along the frequency dimension for the high-resolution spectral features with global temporal information, the second U-Net keeps the frequency dimension and extracts features along the time dimension for the high-resolution temporal features with global spectral information, and the third U-Net downsamples and upsamples along both the frequency and time dimensions for the high-resolution tempo-spectral features. These three cascaded U-Nets are designed to aggregate local and global features, thereby effectively handling the tempo-spectral information of speech signals. The proposed TFFM in this work outperforms state-of-the-art baselines.

Abstract We propose a framework for classifying acoustic scenes utilizing distributed sound sensor devices capable of sound-to-light conversion, which we term as Blinkies. These Blinkies can convert acoustic signals into varying intensities of light via an inbuilt light-emitting diode. By using Blinkies, we can aggregate the spatial acoustic information across a wide region by recording the fluctuating light intensities of numerous Blinkies distributed throughout the region. Nonetheless, the signal communicated is subject to the bandwidth limitation imposed by the frame rate of the video camera, typically capped at 30 frames per second. Our objective is to refine the process of transforming sound into light for the purpose of acoustic scene classification within these bandwidth confines. While traversing through the air, a light signal is affected by inherent physical limitations such as the attenuation of light and interference from noise. To account for these factors, we have integrated these physical constraints into differentiable physical layers. This approach enables us to jointly train a pair of deep neural networks for the conversion of sound to light and for the classification of acoustic scenes. Our simulation studies, which employed the SINS database for acoustic scene classification, demonstrated that our proposed framework outperforms the previous one that utilized Blinkies. These findings emphasize the effectiveness of Blinkies in the field of acoustic scene classification.

Abstract Nowadays, the application of artificial intelligence (AI) algorithms and techniques is ubiquitous and transversal. Fields that take advantage of AI advances include sound and music processing. The advances in interdisciplinary research potentially yield new insights that may further advance the AI methods in this field. This special issue aims to report recent progress and spur new research lines in AI-driven sound and music processing, especially within interdisciplinary research scenarios.

Abstract Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based approaches, particularly in sound field reconstruction. Physics-informed neural networks (PINNs) have emerged as a novel framework, bridging the gap between data-driven and model-based techniques for addressing physical phenomena governed by partial differential equations. This paper introduces a PINN-based approach for the recovery of arbitrary volumetric acoustic fields. The network incorporates the wave equation to impose a regularization on signal reconstruction in the time domain. This methodology enables the network to learn the physical law of sound propagation and allows for the complete characterization of the sound field based on a limited set of observations. The proposed method’s efficacy is validated through experiments involving speech signals in a real-world environment, considering varying numbers of available measurements. Moreover, a comparative analysis is undertaken against state-of-the-art frequency domain and time domain reconstruction methods from existing literature, highlighting the increased accuracy across the various measurement configurations.

Abstract Room impulse responses (RIRs) are used in several applications, such as augmented reality and virtual reality. These applications require a large number of RIRs to be convolved with audio, under strict latency constraints. In this paper, we consider the compression of RIRs, in conjunction with fast time-domain convolution. We consider three different methods of RIR approximation for the purpose of RIR compression and compare them to state-of-the-art compression. The methods are evaluated using several standard objective quality measures, both channel-based and signal-based. We also propose a novel low-rank-based algorithm for fast time-domain convolution and show how the convolution can be carried out without the need to decompress the RIR. Numerical simulations are performed using RIRs of different lengths, recorded in three different rooms. It is shown that compression using low-rank approximation is a very compelling option to the state-of-the-art Opus compression, as it performs as well or better than on all but one considered measure, with the added benefit of being amenable to fast time-domain convolution.

Abstract A kernel interpolation method for the acoustic transfer function (ATF) between regions constrained by the physics of sound while being adaptive to the data is proposed. Most ATF interpolation methods aim to model the ATF for fixed source by using techniques that fit the estimation to the measurements while not taking the physics of the problem into consideration. We aim to interpolate the ATF for a region-to-region estimation, meaning we account for variation of both source and receiver positions. By using a very general formulation for the reproducing kernel function, we have created a kernel function that considers both directed and residual fields as two separate kernel functions. The directed field kernel considers a sparse selection of reflective field components with large amplitudes and is formulated as a combination of directional kernels. The residual field is composed of the remaining densely distributed components with lower amplitudes. Its kernel weight is represented by a universal approximator, a neural network, in order to learn patterns from the data freely. These kernel parameters are learned using Bayesian inference both under the assumption of Gaussian priors and by using a Markov chain Monte Carlo simulation method to perform inference in a more directed manner. We compare all established kernel formulations with each other in numerical simulations, showing that the proposed kernel model is capable of properly representing the complexities of the ATF.

Abstract The estimation sound fields over space is of interest in sound field control and analysis, spatial audio, room acoustics and virtual reality. Sound fields can be estimated from a number of measurements distributed over space yet this remains a challenging problem due to the large experimental effort required. In this work we investigate sensor distributions that are optimal to estimate sound fields. Such optimization is valuable as it can greatly reduce the number of measurements required. The sensor positions are optimized with respect to the parameters describing a sound field, or the pressure reconstructed at the area of interest, by finding the positions that minimize the Bayesian Cramér-Rao bound (BCRB). The optimized distributions are investigated in a numerical study as well as with measured room impulse responses. We observe a reduction in the number of measurements of approximately 50% when the sensor positions are optimized for reconstructing the sound field when compared with random distributions. The results indicate that optimizing the sensors positions is also valuable when the vector of parameters is sparse, specially compared with random sensor distributions, which are often adopted in sparse array processing in acoustics.

Abstract This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) with almost no increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation, which couples the individual instrument networks, and (iii) combination loss (CL). MDL enables the taking advantage of the frequency- and time-domain representations of audio signals. We modify the target network, i.e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information. MDL is then applied to the combinations of the output sources as well as each independent source; hence, we called it CL. MDL and CL can easily be applied to many DNN-based separation methods as they are merely loss functions that are only used during training and do not affect the inference step. Bridging operation does not increase the number of learnable parameters in the network. Experimental results showed that the validity of Open-Unmix (UMX), densely connected dilated DenseNet (D3Net) and convolutional time-domain audio separation network (Conv-TasNet) extended with our X-scheme, respectively called X-UMX, X-D3Net and X-Conv-TasNet, by comparing them with their original versions. We also verified the effectiveness of X-scheme in a large-scale data regime, showing its generality with respect to data size. X-UMX Large (X-UMXL), which was trained on large-scale internal data and used in our experiments, is newly available at https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX .

Abstract End-to-end (E2E) automatic speech recognition (ASR) models, which consist of deep learning models, are able to perform ASR tasks using a single neural network. These models should be trained using a large amount of data; however, collecting speech data which matches the targeted speech domain can be difficult, so speech data is often used that is not an exact match to the target domain, resulting in lower performance. In comparison to speech data, in-domain text data is much easier to obtain. Thus, traditional ASR systems use separately trained language models and HMM-based acoustic models. However, it is difficult to separate language information from an E2E ASR model because the model learns both acoustic and language information in an integrated manner, making it very difficult to create E2E ASR models for specialized target domain which are able to achieve sufficient recognition performance at a reasonable cost. In this paper, we propose a method of replacing the language information within pre-trained E2E ASR models in order to achieve adaptation to a target domain. This is achieved by deleting the “implicit” language information contained within the ASR model by subtracting the source-domain language model trained with a transcription of the ASR’s training data in a logarithmic domain. We then integrate a target domain language model through addition in the logarithmic domain. This subtraction and addition to replace of the language model is based on Bayes’ theorem. In our experiment, we first used two datasets of the Corpus of Spontaneous Japanese (CSJ) to evaluate the effectiveness of our method. We then we evaluated our method using the Japanese Newspaper Article Speech (JNAS) and CSJ corpora, which contain audio data from the read speech and spontaneous speech domain, respectively, to test the effectiveness of our proposed method at bridging the gap between these two language domains. Our results show that our proposed language model replacement method achieved better ASR performance than both non-adapted (baseline) ASR models and ASR models adapted using the conventional Shallow Fusion method.

Abstract The disparities in phonetics and corpuses across the three major dialects of Tibetan exacerbate the difficulty of a single task model for one dialect to accommodate other different dialects. To address this issue, this paper proposes task-diverse meta-learning. Our model can acquire more comprehensive and robust features, facilitating its adaptation to the variations among different dialects. This study uses Tibetan dialect ID recognition and Tibetan speaker recognition as the source tasks for meta-learning, which aims to augment the ability of the model to discriminate variations and differences among different dialects. Consequently, the model’s performance in Tibetan multi-dialect speech recognition tasks is enhanced. The experimental results show that task-diverse meta-learning leads to improved performance in Tibetan multi-dialect speech recognition. This demonstrates the effectiveness and applicability of task-diverse meta-learning, thereby contributing to the advancement of speech recognition techniques in multi-dialect environments.

Abstract This paper introduces a simplified and controllable model for mode coupling in the context of modal synthesis. The model employs efficient coupled filters for sound synthesis purposes, intended to emulate the generation of sounds radiated by sources under strongly nonlinear conditions. Such filters generate tonal components in an interdependent way and are intended to emulate realistic perceptually salient effects in musical instruments in an efficient manner. The control of energy transfer between the filters is realized through a coupling matrix. The generation of prototypical sounds corresponding to nonlinear sources with the filter bank is presented. In particular, examples are proposed to generate sounds corresponding to impacts on thin structures and to the perturbation of the vibration of objects when it collides with an other object. The sound examples presented in the paper and available for listening on the accompanying site illustrate that a simple control of the input parameters allows the generation of sounds whose evocation is coherent and that the addition of random processes yields a significant improvement to the realism of the generated sounds.

Abstract End-to-end speech to text translation aims to directly translate speech from one language into text in another, posing a challenging cross-modal task particularly in scenarios of limited data. Multi-task learning serves as an effective strategy for knowledge sharing between speech translation and machine translation, which allows models to leverage extensive machine translation data to learn the mapping between source and target languages, thereby improving the performance of speech translation. However, in multi-task learning, finding a set of weights that balances various tasks is challenging and computationally expensive. We proposed an adaptive multi-task learning method to dynamically adjust multi-task weights based on the proportional losses incurred during training, enabling adaptive balance in multi-task learning for speech to text translation. Moreover, inherent representation disparities across different modalities impede speech translation models from harnessing textual data effectively. To bridge the gap across different modalities, we proposed to apply optimal transport in the input of end-to-end model to find the alignment between speech and text sequences and learn the shared representations between them. Experimental results show that our method effectively improved the performance on the Tibetan-Chinese, English-German, and English-French speech translation datasets.

Abstract Dysarthria is a speech disorder that affects the ability to communicate due to articulation difficulties. This research proposes a novel method for automatic dysarthria detection (ADD) and automatic dysarthria severity level assessment (ADSLA) by using a variable continuous wavelet transform (CWT) layered convolutional neural network (CNN) model. To determine their efficiency, the proposed model is assessed using two distinct corpora, TORGO and UA-Speech, comprising both dysarthria patients and healthy subject speech signals. The research study explores the effectiveness of CWT-layered CNN models that employ different wavelets such as Amor, Morse, and Bump. The study aims to analyze the models’ performance without the need for feature extraction, which could provide deeper insights into the effectiveness of the models in processing complex data. Also, raw waveform modeling preserves the original signal’s integrity and nuance, making it ideal for applications like speech recognition, signal processing, and image processing. Extensive analysis and experimentation have revealed that the Amor wavelet surpasses the Morse and Bump wavelets in accurately representing signal characteristics. The Amor wavelet outperforms the others in terms of signal reconstruction fidelity, noise suppression capabilities, and feature extraction accuracy. The proposed CWT-layered CNN model emphasizes the importance of selecting the appropriate wavelet for signal-processing tasks. The Amor wavelet is a reliable and precise choice for applications. The UA-Speech dataset is crucial for more accurate dysarthria classification. Advanced deep learning techniques can simplify early intervention measures and expedite the diagnosis process.

Abstract Polyphonic sound source localization and detection (SSLD) task aims to recognize the categories of sound events, identify their onset and offset times, and detect their corresponding direction-of-arrival (DOA), where polyphonic refers to the occurrence of multiple overlapping sound sources in a segment. However, vanilla SSLD methods based on convolutional recurrent neural network (CRNN) suffer from insufficient feature extraction. The convolutions with kernel of single scale in CRNN fail to adequately extract multi-scale features of sound events, which have diverse time-frequency characteristics. It results in that the extracted features lack fine-grained information helpful for the localization of sound sources. In response to these challenges, we propose a polyphonic SSLD network based on global-local feature extraction and recalibration (GLFER-Net), where the global-local feature (GLF) extractor is designed to extract the multi-scale global features through an omni-directional dynamic convolution (ODConv) layer and multi-scale feature extraction (MSFE) module. The local feature extraction (LFE) unit is designed for capturing detailed information. Besides, we design a feature recalibration (FR) module to emphasize the crucial features along multiple dimensions. On the open datasets of Task3 in DCASE 2021 and 2022 Challenges, we compared our proposed GLFER-Net with six and four SSLD methods, respectively. The results show that the GLFER-Net achieves competitive performance. The modules we designed are verified to be effective through a series of ablation experiments and visualization analyses.

Abstract While deep learning technologies have made remarkable progress in generating deepfakes, their misuse has become a well-known concern. As a result, the ubiquitous usage of deepfakes for increasing false information poses significant risks to the security and privacy of individuals. The primary objective of audio spoofing detection is to identify audio generated through numerous AI-based techniques. Several techniques for fake audio detection already exist using machine learning algorithms. However, they lack generalization and may not identify all types of AI-synthesized audios such as replay attacks, voice conversion, and text-to-speech (TTS). In this paper, a deep layered model, i.e., VGGish, along with an attention block, namely Convolutional Block Attention Module (CBAM) for spoofing detection, is introduced. Our suggested model successfully classifies input audio into two classes: Fake and Real, converting them into mel-spectrograms, and extracting their most representative features due to the attention block. Our model is a significant technique to utilize for audio spoofing detection due to a simple layered architecture. It captures complex relationships in audio signals due to both spatial and channel features present in an attention module. To evaluate the effectiveness of our model, we have conducted in-depth testing using the ASVspoof 2019 dataset. The proposed technique achieved an EER of 0.52% for Physical Access (PA) attacks and 0.07 % for Logical Access (LA) attacks.

Abstract A new method for estimating the first and second derivatives of discrete audio signals intended to achieve higher computational precision in analyzing the performance and characteristics of digital audio systems is presented. The method could find numerous applications in modeling nonlinear audio circuit systems, e.g., for audio synthesis and creating audio effects, music recognition and classification, time-frequency analysis based on nonstationary audio signal decomposition, audio steganalysis and digital audio authentication or audio feature extraction methods. The proposed algorithm employs the ordinary 7 point-stencil central-difference formulas with improvements that minimize the round-off and truncation errors. This is achieved by treating the step size of numerical differentiation as a regularization parameter, which acts as a decision threshold in all calculations. This approach requires shifting discrete audio data by fractions of the initial sample rate, which was obtained by fractional delay FIR filters designed with modified 11-term cosine-sum windows for interpolation and shifting of audio signals. The maximum relative error in estimating first and second derivatives of discrete audio signals are respectively in order of $$10^{-13}$$ 10 - 13 and $$10^{-10}$$ 10 - 10 over the entire audio band, which is close to double-precision floating-point accuracy for the first and better than single-precision floating-point accuracy for the second derivative estimation. Numerical testing showed that this performance of the proposed method is not influenced by the type of signal being differentiated (either stationary or nonstationary), and provides better results than other known differentiation methods, in the audio band up to 21 kHz.

Abstract This work introduces a large dataset comprising impulse responses of spatially distributed sources within a plane parallel to a planar microphone array. The dataset, named MIRACLE, encompasses 856,128 single-channel impulse responses and includes four different measurement scenarios. Three measurement scenarios were conducted under anechoic conditions. The fourth scenario includes an additional specular reflection from a reflective panel. The source positions were obtained by uniformly discretizing a rectangular source plane parallel to the microphone for each scenario. The dataset contains three scenarios with a spatial resolution of $$23\,\textrm{mm}$$ 23 mm at two different source-plane-to-array distances, as well as a scenario with a resolution of $$5\,\textrm{mm}$$ 5 mm for the shorter distance. In contrast to existing room impulse response datasets, the accuracy of the provided source location labels is assessed and additional metadata, such as the directivity of the loudspeaker used for excitation, is provided. The MIRACLE dataset can be used as a benchmark for data-driven modelling and interpolation methods as well as for various acoustic machine learning tasks, such as source separation, localization, and characterization. Two timely applications of the dataset are presented in this work: the generation of microphone array data for data-driven source localization and characterization tasks and data-driven model order reduction.

Abstract Time signature detection is a fundamental task in music information retrieval, aiding in music organization. In recent years, the demand for robust and efficient methods in music analysis has amplified, underscoring the significance of advancements in time signature detection. In this study, we explored the effectiveness of residual networks for time signature detection. Additionally, we compared the performance of the residual network (ResNet18) to already existing models such as audio similarity matrix (ASM) and beat similarity matrix (BSM). We also juxtaposed with traditional algorithms such as support vector machine (SVM), random forest, K-nearest neighbor (KNN), naive Bayes, and that of deep learning models, such as convolutional neural network (CNN) and convolutional recurrent neural network (CRNN). The evaluation is conducted using Mel-frequency cepstral coefficients (MFCCs) as feature representations on the Meter2800 dataset. Our results indicate that ResNet18 outperforms all other models thereby showing the potential of deep learning models for accurate time signature detection.

Abstract Limited data availability remains a significant challenge for Whisper’s low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully reduced the recognition error rates of target language speech through fine-tuning, a comprehensive exploration and analysis of Whisper’s fine-tuning capabilities and the advantages and disadvantages of various fine-tuning strategies are still lacking. This paper aims to fill this gap by conducting comprehensive experimental exploration for Whisper’s low-resource speech recognition performance using five fine-tuning strategies with limited supervised data from seven low-resource languages. The results and analysis demonstrate that all fine-tuning strategies explored in this paper significantly enhance Whisper’s performance. However, different strategies vary in their suitability and practical effectiveness, highlighting the need for careful selection based on specific use cases and resources available.

Melon seeds have received considerable attention in recent years because of their high protein content, but they have not yet been fully used. The modification of melon seed protein (MSP) using ultrasound-assisted pH-shifting treatment was investigated in this study by analyzing structural characteristics and functional properties. The particle size, free sulfhydryl content, surface hydrophobicity, solubility, secondary structure, water-holding capacity, oil-holding capacity, emulsification activity index, and emulsification stability index of MSP were determined. MSP treated with ultrasound-assisted, pH-shifting had a smaller particle size, lower free sulfhydryl content, higher surface hydrophobicity, and solubility increased from 43.67 % to 89.12 %. The secondary structure of MSP was affected by ultrasonic treatment, manifesting as an α-helix increase and β-helix, β-turn, and random coil content decrease, which may be the reason why the protein structure became more compact after treatment. The water and oil holding capacities of MSP increased from 2.74 g/g and 3.14 g/g in untreated samples to 3.19 g/g and 3.97 g/g for ultrasound-treated samples, and further increased to 3.97 g/g and 5.02 g/g for ultrasound-assisted, pH-shifting treatment at pH 9.0, respectively. The emulsification activity index of MSP was 21.11 m2/g before treatment and reached a maximum of 32.34 m2/g after ultrasound-assisted, pH-shifting treatment at pH 9.0. The emulsification stability of MSP was maximized by ultrasonic treatment at pH 7.0. Ultrasound-assisted, pH-shifting treatment can effectively improve the functional properties of MSP by modifying the protein structure, which improves the potential application of melon seed protein in the food industry.

Chili peppers (Capsicum spp.) exhibit a diverse range of quality characteristics and pectin structures, which are influenced by various factors. This study aimed to investigate the effects of ultrasound (US), ultrasonic combined hot blanching (US-BL), and ultrasonic combined freezing and thawing (US-FT) on the quality characteristics and pectin structure of vacuum pulsation-dried (VP) chili peppers. The results indicated that US-BL samples exhibited the highest L* and a* values, retained maximum capsorubin, and showed an increase in vitamin C, total phenols, and rehydration by 14.28 %, 40.87 %, and 8.66 %, respectively. In contrast, the US-FT samples exhibited the highest capsaicin and dihydrocapsaicin content, which increased by 54.97 % and 64.04 %, respectively. Pretreatment resulted in higher pectin linearity, a lower degree of branching, and a reduced molecular weight in the US-BL sample. Atomic force microscopy confirmed the degrading effect of pretreatment on the pectin structure. Pearson’s correlation analysis revealed that capsorubin, capsaicin analogs, vitamin C, and total phenols were highly correlated with pectin linearity and molecular weight. This study found that US-BL was the most effective pretreatment method for improving the quality of pulsatile chili peppers and provides theoretical support for the application of VP chili peppers.

Drying process extends the shelf-life of fresh faba beans and makes them available all year round. Dried and cooked faba beans are used to make a variety of traditional food products. Ultrasonic pretreatment, as a modern food processing technology, can shorten the drying time of fresh legumes and improve the quality and sensory properties of products. So, the present study aimed to analyze the impact of the ultrasonic treatment process (0, 5, 10, and 15 min, 40 kHz, and 150 W) on the mass transfer rate, drying time, and effective moisture diffusivity (Deff) of fresh faba beans. Also, the effect of ultrasonic treatment on textural properties and sensory attributes of cooked faba beans was studied. By using the ultrasonic process, the rate of water extraction from fresh faba beans, and thus their dehydration rate, can be increased. With increasing the duration of ultrasonic pretreatment from 0 to 15 min, the drying time of fresh faba beans decreased from 250 min to 150 min (p 0.9968, and the sum of squared error (SSE) and root mean squared error (RMSE) were also closer to zero compared to other models. The rehydration ratio of dried faba beans (after cooking) significantly increased from 308.4 % to 327.1 % with the extension of processing time from 0 to 15 min (p

In this study, it is the first that the Viticis Fructus (VF) was used as the raw material for extracting total flavonoids using the ultrasound-assisted enzyme extraction (UAE) method. Response surface methodology was employed to determine the optimal extraction parameters. The optimal conditions were as follows: 60 % ethanol solution as the extract solvent, material–liquid ratio of 1:25, pH value of 4, enzyme addition amount of 1.5 %, enzymatic hydrolysis time of 30 min, enzymatic hydrolysis temperature of 40 ℃, and ultrasonic time of 50 min. Comparing the total flavonoid yield of VF and processed VF (PVF) extracted using different methods, it was observed that UAE resulted in a higher total flavonoid yield compared to traditional ultrasound extraction and enzyme extraction. Additionally, the total flavonoid yield of PVF extracted by all three methods was generally higher than that of VF. The PVF solution extracted by UAE also demonstrated better in vitro antioxidant activity compared to VF. These results suggest that UAE is an effective method to enhance the activity of natural total flavonoids. The study of the physicochemical properties and in vitro antioxidant activity of VF and PVF showed that the total flavonoid yield and antioxidant activity significantly increased after VF stir-frying, indicating that their efficacy can also be enhanced.

Air-dried beef, a traditional dry fermented meat product in China, whose quality is largely influenced by processing conditions. In this study, contact ultrasound (CU) and infrared radiation (IR) were employed to enhance hot air drying (HAD), with an investigation into the mechanisms underlying improvements in quality and flavor. Samples subjected to CU and IR treatments during HAD (CU-IRD) demonstrated superior color (L* = 42.68, a* = 5.05, b* = −3.86) and tenderness (140.59 N) than HAD group, primarily attributed to reduced drying times and alterations in ultrastructure. Analyses utilizing SDS-PAGE and total volatile basic nitrogen (TVB-N) revealed that HAD and CU-HAD resulted in significant protein oxidation (197.85 mg TVB-N/kg and 202.23 mg TVB-N/kg, respectively), while IR treatments were associated with increased thermal degradation of proteins, producing lower molecular weight peptides. Compared with HAD group, the activities of certain lipases and proteases were enhanced by ultrasound and infrared treatments, leading to the release of greater amounts of free fatty acids and flavor amino acids. Furthermore, the thermal effects of infrared and the cavitation effects of ultrasound contributed to increased fat oxidation, amino acid Strecker degradation, and esterification reactions, thereby augmenting the diversity and concentration of volatile flavor compounds, including alkanes, ketones, aldehydes, and esters. These findings indicate that the synergistic application of CU and IR represents a promising strategy for enhancing the quality of air-dried beef.

In this study, ultrasound-enhanced calcium carbonate precipitation was used to enrich indium in zinc oxide dust leachate, and the effects of precipitation endpoint pH and ultrasound power on the indium precipitation behaviour were investigated, and the optimal conditions of ultrasound-enhanced precipitation were obtained to be the precipitation endpoint pH of 4.0 and the ultrasound power of 200 W. The precipitation rate of indium under these conditions was 99.79 %. At the same time, the effects of ultrasonication and conventional stirring on the indium precipitation kinetics were compared, which proved that ultrasound can shorten the time for precipitation to reach equilibrium and reduce the amount of calcium carbonate used, and the theory of ultrasonication activation energy was put forward. The activation energy of ultrasonication was Eu-a = 2.63 KJ/mol, and that of conventional precipitation was 9.78KJ/mol, which proved that ultrasonication could reduce the activation energy of the precipitation reaction, and promote the rapid precipitation reaction. The kinetic model of ultrasound-enhanced indium precipitation is lnC0-lnCt = exp(0.11339–318.54/W).t + A. In addition, the mechanism of ultrasound-enhanced calcium carbonate precipitation of indium was revealed by XRD, SEM-EDS, XPS and TEM analyses of the precipitated residue, it was demonstrated that ultrasound can inhibit the precipitation of zinc, and the ZnCO3 phase was found in the ultrasonically precipitated residue. This study provides a new idea for indium enrichment, and the future focus will be on the scale-up of the ultrasound-enhanced precipitation device.

The accumulation of electric arc furnace slag (EAFS) in landfills has been causing severe environmental problems. This study examines the dissolution properties of EAFS minerals, including brownmillerite and gehlenite, essential for their possible use in resource recovery. An investigation was conducted to compare the effects of sonication and stirring on mineral dissolution while also assessing the usage of citrate as a complexing agent for gehlenite. Synthetic brownmillerite and gehlenite minerals were dissolved in aqueous solutions at room temperature using a 1:100 g/ml ratio. The dissolved elements were measured using Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), while zeta potential and X-ray Photoelectron Spectroscopy (XPS) were used to assess changes in surface chemistry. Brownmillerite had significant dissolution extents, with Al and Ca dissolving up to 16 % and 8 %, respectively, in contrast to gehlenite, which dissolved less than 2 % under similar conditions. Sonication significantly increased the dissolution of brownmillerite by up to 100 %, although its impact on gehlenite dissolution varied depending on the duration of time. Besides, adding citrate enhanced the leaching of Al and Ca from gehlenite by facilitating complexation. XPS data demonstrated differences in elemental ratios on brownmillerite and gehlenite surfaces affected by the method used and the presence of citrate. Lastly, the dissolution extents of Al and Ca from EAFS were up to 12 %, depending on time and mixing method, with a preference for sonication over stirring. In conclusion, this study showed that minerals in EAFS have distinct dissolution characteristics, and sonication and citrate can considerably enhance dissolution.

Ultrasound is gaining attention as an alternative tool to regulate chemical processes due to its advantages such as high cost-effectiveness, rapid response, and contact-free operation. Previous studies have demonstrated that acoustic bubble cavitation can generate energy to synthesize functional materials. In this study, we introduce a method to control the spatial distribution of physical and chemical properties of hydrogels by using an ultrasound-mediated particle manipulation technique. We developed a surface acoustic wave device that can localize micro-hydrogel particles, which are formed during gelation, in a hydrogel solution. The hydrogel fabricated with the application of surface acoustic waves exhibited gradients in mechanical, mass transport, and structural properties. We demonstrated that the gel having the property gradients could be utilized as a cell-culture substrate dictating cellular shapes, which is beneficial for interfacial tissue engineering. The acoustic method and fabricated hydrogels with property gradients can be applied to design flexible polymeric materials for soft robotics, biomedical sensors, or bioelectronics applications.

Low-temperature cooking causes flavor weakness while improving the texture and digestive properties of meat. To enhance the flavor of low-temperature cooked Tibetan pork, samples were cooked at low-temperature with or without ultrasound-assisted (UBTP, BTP) for different times (30 min, 90 min) and then analyzed using GC–MS and LC-MS. The results showed that ultrasound-assisted cooking caused a significant increase in lipid oxidation by 9.10% in the early stage of the treatment. Additionally, at the later stage of ultrasound-assisted processing, proteins were oxidized and degraded, which resulted in a remarkable rise in the protein carbonyl content by 6.84%. With prolonged effects of ultrasound and low-temperature cooking, the formation of phenylacetaldehyde in UBTP-90 sample originated from the degradation of phenylalanine through multivariate statistics and correlation analysis. Meanwhile, trans, cis-2,6-nonadienal and 1-octen-3-one originated from the degradation of linolenic acid and arachidonic acid. This study clarified the mechanism of ultrasound-assisted treatment improving the flavor of low-temperature-cooked Tibetan pork based on the perspective of lipids and proteins oxidation, providing theoretical supports for flavor enhancement in Tibetan pork-related products.

Sonoprocessing (US), as one of the most well-known and widely used green processing techniques, has tremendous benefits to be used in the food industry. The urgent call for global sustainable food production encourages the usage of such techniques more often and effectively. Using ultrasound as a hurdle technology synergistically with other green methods is crucial to improving the efficiency of the protein shift as well as the number of plant-based analog foods (PBAFs) against conventional products. It was revealed that the US has a significant impact when used as an assistant tool with other green technologies rather than being used alone. It increases the protein extraction efficiencies from plant biomasses, improves the techno-functional properties of food compounds, and makes them more applicable for industrial-scale alternative food production in the circular economy. The US aligns well with the objectives outlined in the UN’s Sustainable Development Goals (SDGs), and Planetary Boundaries (PBs) framework, demonstrating promising outcomes in life cycle assessment. However, several challenges such as uncontrolled complex matrix effect, free radical formation, uncontrolled microbial growth/germination or off-flavor formation, removal of aromatic compounds, and Maillard reaction, are revealed in an increased number of studies, all of which need to be considered. In addition to a variety of advantages, this review also discusses the drawbacks and limitations of US focusing on PBAF production.

The aim of the present study was to evaluate the effects of ultrasound-assisted intermittent tumbling (UT) at 300 W, 20 kHz and 40 min on the conformation, intermolecular interactions and aggregation of myofibrillar proteins (MPs) and its induced gelation properties at various tumbling times (4 and 6 h). Raman results showed that all tumbling treatments led the helical structure of MPs to unfold. In comparison to the single intermittent tumbling treatment (ST), UT treatment exerted more pronounced effects on strengthening the intermolecular hydrogen bonds and facilitating the formation of an ordered β-sheet structure. When the tumbling time was the same, UT treatment caused higher surface hydrophobicity, fluorescence intensity and disulfide bond content in the MPs, inducing the occurrence of hydrophobic interaction and disulfide cross-linking between MPs molecules, thus forming the MPs aggregates. Additionally, results from the solubility, particle size, atomic force microscopy and SDS-PAGE further indicated that, relative to the ST treatment, UT treatment was more potent in promoting the polymerization of myosin heavy chain. The MPs aggregates in the UT group were more uniform than those in the ST group. During the gelation process, the pre-formed MPs aggregates in the UT treatment increased the thermal stability of myosin, rendering it more resistant to heat-induced unfolding of the myosin rod region. Furthermore, they improved the protein tail–tail interaction, resulting in the formation of a well-structured gel network with higher gel strength and cooking yield compared to the ST treatment.

Lycopene is a carotenoid highly valuable to the food, pharmaceutical, dye, and cosmetic industries, present in ripe tomatoes and other fruits with a distinctive red color. The main source of lycopene is tomato crops. This bioactive component can be successfully isolated from tomato processing waste, commonly called tomato pomace, mostly made from tomato skins, seeds, and some residual tomato tissue. The main investigative focus in this work was the application of green engineering principles in each stage of the optimized ultrasound-assisted extraction (UAE) of enzymatically treated tomato skins to obtain functional extracts rich in lycopene. The experimental plan was designed to determine the influence of studied operating parameters: enzymatic reaction time (60, 120, and 180 min), extraction time (0, 5, 10, 15, 30, 60, and 120 min), and temperature (25, 35 and 45 ℃) on lycopene yield. Process optimization was performed based on the yield of lycopene [1018, 1067, and 1120 mg/kg] achieved at optimal operating conditions. An artificial neural network (ANN) model was developed and trained for predictive modeling of the closed extraction system, with operating parameters used as input neurons and experimentally obtained values for lycopene content defined as the output neural layer. Applied ANN architecture provided a high correlation of experimental output with ANN-generated data (R=0.99914) with a model deviation error for the entire data set of RMSE=5.3 mg/kg. The k-Nearest Neighbor algorithm was introduced to predict lycopene yield using experimental key features: operating temperature, extraction time, and time of enzymatic treatment, split into training and testing sets with an 85/15 ratio. The model interpretation was conducted through the SHAP (SHapley Additive exPlanations) methodology.

Tubocapsicum anomalum, a Chinese medicinal plant rich in anti-tumor withanolides, requires efficient extraction methods. In this paper, an HPLC method was first established for the detection of withanolides, and gradient elution was carried out using a methanol–water solvent system. It was found that the content of withanolides was the highest in the leaves of T. anomalum, followed by the stems and fruits, and almost none in the roots. During the actual picking process, the quantity of leaves collected was relatively small, while the number of stems was the highest. Therefore, the Box-Behnken response surface method was used to optimize the ultrasonic-assisted extraction process of withanolides from the stems of T. anomalum. The optimal extraction conditions were determined as follows: the liquid–solid ratio was 20:1, the extraction solvent was 70 % ethanol, the ultrasonic power was 250 W, the ultrasonic time was 40 min, and the ultrasonic temperature was 50 °C. Under these conditions, the average yields of tubocapsenolide A (Te-A) and tubocapsanolide A (Ta-A) can reach 2.87 ± 0.12 mg/g and 1.18 ± 0.05 mg/g, respectively. We further compared extraction rates of two withanolides from different parts of T. anomalum using ultrasonic and traditional extraction methods. Ultrasonic extraction significantly increased rates, with the highest yields from leaves, followed by stems and fruits. The results show that ultrasonic optimization can improve extraction rate, reduce time, lower costs, enhance quality, and increase yield. Therefore, the optimized ultrasonic-assisted extraction process was adopted to extract the aerial parts of T. anomalum and separate the components. After optimization, the extract underwent several chromatographic separations to isolate eight previously undescribed withanolides (1–8) and two artificial withanolides (9–10), in addition to fifteen known compounds (11–25). Their structures were established through extensive spectroscopic data analysis. The compounds were evaluated for their antiproliferative effects against multiple cancer cell lines, including human hepatocellular carcinoma cells (HepG2, Hep3B, and MHCC97-H), human lung cancer cells (A549), human fibro-sarcoma cancer cells (HT1080), human chronic myeloid leukemia cells (K562), and human breast cancer cells (MDA-MB-231 and MCF7). Compounds 1–3, 5, 7, 11, 13, 15–16, and 22 displayed significant activity with IC50 values of 5.14–19.87 μM. The above results indicate that ultrasonic-assisted extraction technology can be used to obtain new withanolides more efficiently from T. anomalum, thereby enhancing the utilization rate of T. anomalum resources.

The present study aims at investigating the application of ultrasound assisted choline chloride (ChCl) – formic acid (FA) deep eutectic solvent (DES) pretreatment of Barley straw. In addition, the efficiency of a wet grinding followed by high intensity ultrasound (HIUS) treatment for production of cellulose nanofibers (CNF) has been evaluated. The DES (using ChCl: FA at 1:9 M ratio) treatment at 45 kHz ultrasound frequency and 3 h of treatment duration resulted in 84.68 ± 1.02 % and 82.96 ± 0.79 % of lignin and hemicellulose solubilisation, respectively. The purification of DES treated solid residue resulted in cellulose with more than 90 % purity. Further, 10 min of wet grinding followed by 40 min of HIUS treatment resulted in more than 80 % nano-fibrillation efficiency. The produced CNF had diameters less than 100 nm in number size distribution and type I cellulose structure. This study confirmed that the developed process offers a sustainable method for producing nanocellulose from agricultural waste.

Ultrasonic delamination is a low energy approach for direct recycling of spent lithium-ion batteries. The efficiency of the ultrasonic delamination relies both on the thermophysical properties (such as viscosity, surface tension, and vapour pressure) of the solvent in which the delamination process is carried out, and the properties of the ultrasound source as well as the geometry of the containment vessel. However, the effect of tailoring solutions to optimise cavitation and delamination of battery cathode coatings has not yet been sufficiently investigated. Acoustic detection, high-speed imaging, and sonochemiluminescence (SCL) are employed to study the cavitation processes in water-glycol systems and identify the effect of tailoring solvent composition on cavitation strength. The addition of small volume fractions of organic solvent (ca. 10–30 vol%), including ethylene glycol or glycerol, to the aqueous delamination solution were found to significantly improve the delamination efficiency of lithium-ion battery cathode coatings due to the alteration of these thermophysical properties. However, greater volume fractions of glycol decrease delamination efficiency due to the signal-dampening effect of viscosity on the ultrasonic waves. The findings of this study offer valuable insights for optimising ultrasonic bath solution composition to enhance film delamination processes.

In order to address the problem of poor localization in electrochemical machining (ECM), a coaxial megasonic assisted jet ECM method was proposed. Based on theoretical analysis, experiments were conducted to compare the effects of various electrolyte flow rates, electrolytic voltage and megasonic power levels on pit ECM. The results indicate that, in the range of experimental parameters, the increase of electrolyte flow rate and megasonic power can increase the machining depth, so as to improve the depth-diameter ratio of ECM pits. The use of coaxial megasonic-assisted jet ECM can enhance the depth-diameter ratio of etched pits compared to the without megasonic one. When applying a megasonic power of 22 W, the dimensions of the ECM pit were measured as 0.81 mm in depth and 5.73 mm in diameter, resulting in an depth-diameter ratio of 0.140. Under the same conditions, without megasonic assistance, the pit diameter is reduced to 0.65 mm while the pit depth increases to 6.36 mm, resulting in a depth-diameter ratio of 0.102. Additionally, The results also demonstrate that, the increase of electrolytic voltage makes the depth to diameter ratio of pit further increase on the original basis. With an electrolyte flow rate of 0.9 L/min and a megasonic power of 22 W, the use of electrolysis voltage of 50 V increased the depth-diameter ratio of etched pits to 0.173. Using the above preferred parameters, electrolytic milling of the wide groove is carried out. The depth-diameter ratio of the wide groove is increased from 0.039 to 0.059 by appending coaxial megasonic. This further verified the effectiveness of the coaxial megasonic-assisted jet ECM method.

Based on sonochemistry, green synthesis methods play an important role in the development of nanomaterials. In this work, a novel chitosan modified MnMoO4/g-C3N4 (MnMoO4/g-C3N4/CHIT) was developed using ultrasonic cell disruptor (500 W, 30 kHz) for ultra-sensitive electrochemical detection of tinidazole (TNZ) in the environment. The morphology and surface properties of the synthesized MnMoO4/g-C3N4/CHIT electrode were characterized using X-ray diffraction (XRD), fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM) and transmission electron microscope (TEM). Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques were utilized to assess the electrochemical performance of TNZ. The results indicate that the electrochemical detection performance of TNZ is highly efficient, with a detection limit (LOD) of 3.78 nM, sensitivity of 1.320 µA·µM−1·cm−2, and a detection range of 0.1–200 μM. Additionally, the prepared electrode exhibits excellent selectivity, desirable anti-interference capability, and decent stability. MnMoO4/g-C3N4/CHIT can be successfully employed to detect TNZ in both the Songhua River and tap water, achieving good recovery rates within the range of 93.0 % to 106.6 %. Consequently, MnMoO4/g-C3N4/CHIT’s simple synthesis might provide a new electrode for the sensitive, repeatable, and selective measurement of TNZ in real-time applications. Using the MnMoO4/g-C3N4/CHIT electrode can effectively monitor and detect the concentration of TNZ in environmental water, guiding the sewage treatment process and reducing the pollution level of antibiotics in the water environment.

The radial and translational motions of multiple interacting spherical bubbles are obtained using classical Newton mechanics. It is seen that bubbles not only move in straight line, but also in circular motion. The tracks of the bubbles show that the interactions among them include attractive, repulsive and dynamic equilibrium. There are three types of straight line corresponding to attraction, coexistence of attraction and repulsion and dynamic equilibrium, and two types of circular movement corresponding to attraction and dynamic equilibrium. The results can provide an explanation for cavitation chain and profile in cavitation field.