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Frequency analysis of the photoacoustic radiofrequency signals and oxygen saturation estimates were used to monitor the in-vivo response of a novel, thermosensitive liposome treatment. The liposome encapsulated doxorubicin (HaT-DOX) releasing it rapidly (Photoacoustic imaging (VevoLAZR, 750/850 nm, 40 MHz) of EMT-6 breast cancer tumors was performed 30 min pre- and post-treatment and up to 7 days post-treatment (at 2/5/24 h timepoints). HaT-DOX-treatment responders exhibited on average a 22% drop in oxygen saturation 2 h post-treatment and a decrease (45% at 750 nm and 73% at 850 nm) in the slope of the normalized PA frequency spectra. The spectral slope parameter correlated with treatment-induced hemorrhaging which increased the optical absorber effective size via interstitial red blood cell leakage. Combining frequency analysis and oxygen saturation estimates differentiated treatment responders from non-responders/control animals by probing the treatment-induced structural changes of blood vessel.

In ultrasound imaging, fully-developed speckle arises from the spatiotemporal superposition of pressure waves backscattered by randomly distributed scatterers. Speckle appearance is affected by the imaging system characteristics (lateral and axial resolution) and the random-like nature of the underlying tissue structure. In this work, we examine speckle formation in acoustic-resolution photoacoustic (PA) imaging using simulations and experiments. Numerical and physical phantoms were constructed to demonstrate that PA speckle carries information related to unresolved absorber structure in a manner similar to ultrasound speckle and unresolved scattering structures. A fractal-based model of the tumor vasculature was used to study PA speckle from unresolved cylindrical vessels. We show that speckle characteristics and the frequency content of PA signals can be used to monitor changes in average vessel size, linked to tumor growth. Experimental validation on murine tumors demonstrates that PA speckle can be utilized to characterize the unresolved vasculature in acoustic-resolution photoacoustic imaging. Keywords: Photoacoustic speckle, Ultrasound speckle, Spatial resolution, Autocovarience function, Tumor vasculature, In-vivo imaging, Vascular trees

An integrated version of the minimum variance distortionless response (MVDR) beamformer for speech enhancement using a microphone array has been recently developed, which merges the benefits of imposing constraints defined from both a relative transfer function (RTF) vector based on a priori knowledge and an RTF vector based on a data-dependent estimate. In this paper, the integrated MVDR beamformer is extended for use with a microphone configuration where a microphone array, local to a speech processing device, has access to the signals from multiple external microphones (XMs) randomly located in the acoustic environment. The integrated MVDR beamformer is reformulated as a quadratically constrained quadratic program (QCQP) with two constraints, one of which is related to the maximum tolerable speech distortion for the imposition of the a priori RTF vector and the other related to the maximum tolerable speech distortion for the imposition of the data-dependent RTF vector. An analysis of how these maximum tolerable speech distortions affect the behaviour of the QCQP is presented, followed by the discussion of a general tuning framework. The integrated MVDR beamformer is then evaluated with audio recordings from behind-the-ear hearing aid microphones and three XMs for a single desired speech source in a noisy environment. In comparison to relying solely on an a priori RTF vector or a data-dependent RTF vector, the results demonstrate that the integrated MVDR beamformer can be tuned to yield different enhanced speech signals, which may be more suitable for improving speech intelligibility despite changes in the desired speech source position and imperfectly estimated spatial correlation matrices.

Over the recent years, machine learning techniques have been employed to produce state-of-the-art results in several audio related tasks. The success of these approaches has been largely due to access to large amounts of open-source datasets and enhancement of computational resources. However, a shortcoming of these methods is that they often fail to generalize well to tasks from real life scenarios, due to domain mismatch. One such task is foreground speech detection from wearable audio devices. Several interfering factors such as dynamically varying environmental conditions, including background speakers, TV, or radio audio, render foreground speech detection to be a challenging task. Moreover, obtaining precise moment-to-moment annotations of audio streams for analysis and model training is also time-consuming and costly. In this work, we use multiple instance learning (MIL) to facilitate development of such models using annotations available at a lower time-resolution (coarsely labeled). We show how MIL can be applied to localize foreground speech in coarsely labeled audio and show both bag-level and instance-level results. We also study different pooling methods and how they can be adapted to densely distributed events as observed in our application. Finally, we show improvements using speech activity detection embeddings as features for foreground detection.

We propose an algorithm for the blind separation of single-channel audio signals. It is based on a parametric model that describes the spectral properties of the sounds of musical instruments independently of pitch. We develop a novel sparse pursuit algorithm that can match the discrete frequency spectra from the recorded signal with the continuous spectra delivered by the model. We first use this algorithm to convert an STFT spectrogram from the recording into a novel form of log-frequency spectrogram whose resolution exceeds that of the mel spectrogram. We then make use of the pitch-invariant properties of that representation in order to identify the sounds of the instruments via the same sparse pursuit method. As the model parameters which characterize the musical instruments are not known beforehand, we train a dictionary that contains them, using a modified version of Adam. Applying the algorithm on various audio samples, we find that it is capable of producing high-quality separation results when the model assumptions are satisfied and the instruments are clearly distinguishable, but combinations of instruments with similar spectral characteristics pose a conceptual difficulty. While a key feature of the model is that it explicitly models inharmonicity, its presence can also still impede performance of the sparse pursuit algorithm. In general, due to its pitch-invariance, our method is especially suitable for dealing with spectra from acoustic instruments, requiring only a minimal number of hyperparameters to be preset. Additionally, we demonstrate that the dictionary that is constructed for one recording can be applied to a different recording with similar instruments without additional training.

In this paper, a study addressing the task of tracking multiple concurrent speakers in reverberant conditions is presented. Since both past and future observations can contribute to the current location estimate, we propose a forward-backward approach, which improves tracking accuracy by introducing near-future data to the estimator, in the cost of an additional short latency. Unlike classical target tracking, we apply a non-Bayesian approach, which does not make assumptions with respect to the target trajectories, except for assuming a realistic change in the parameters due to natural behaviour. The proposed method is based on the recursive expectation-maximization (REM) approach. The new method is dubbed forward-backward recursive expectation-maximization (FB-REM). The performance is demonstrated using an experimental study, where the tested scenarios involve both simulated and recorded signals, with typical reverberation levels and multiple moving sources. It is shown that the proposed algorithm outperforms the regular common causal (REM).

Instrumental playing techniques such as vibratos, glissandos, and trills often denote musical expressivity, both in classical and folk contexts. However, most existing approaches to music similarity retrieval fail to describe timbre beyond the so-called "ordinary" technique, use instrument identity as a proxy for timbre quality, and do not allow for customization to the perceptual idiosyncrasies of a new subject. In this article, we ask 31 human subjects to organize 78 isolated notes into a set of timbre clusters. Analyzing their responses suggests that timbre perception operates within a more flexible taxonomy than those provided by instruments or playing techniques alone. In addition, we propose a machine listening model to recover the cluster graph of auditory similarities across instruments, mutes, and techniques. Our model relies on joint time--frequency scattering features to extract spectrotemporal modulations as acoustic features. Furthermore, it minimizes triplet loss in the cluster graph by means of the large-margin nearest neighbor (LMNN) metric learning algorithm. Over a dataset of 9346 isolated notes, we report a state-of-the-art average precision at rank five (AP@5) of $99.0\%\pm1$. An ablation study demonstrates that removing either the joint time--frequency scattering transform or the metric learning algorithm noticeably degrades performance., Comment: 32 pages, 5 figures. To appear in EURASIP Journal on Audio, Speech, and Music Processing (JASMP)

We propose a method of dynamically registering out-of-vocabulary (OOV) words by assigning the pronunciations of these words to pre-inserted OOV tokens, editing the pronunciations of the tokens. To do this, we add OOV tokens to an additional, partial copy of our corpus, either randomly or to part-of-speech (POS) tags in the selected utterances, when training the language model (LM) for speech recognition. This results in an LM containing OOV tokens, to which we can assign pronunciations. We also investigate the impact of acoustic complexity and the “natural” occurrence frequency of OOV words on the recognition of registered OOV words. The proposed OOV word registration method is evaluated using two modern automatic speech recognition (ASR) systems, Julius and Kaldi, using DNN-HMM acoustic models and N-gram language models (plus an additional evaluation using RNN re-scoring with Kaldi). Our experimental results show that when using the proposed OOV registration method, modern ASR systems can recognize OOV words without re-training the language model, that the acoustic complexity of OOV words affects OOV recognition, and that differences between the “natural” and the assigned occurrence frequencies of OOV words have little impact on the final recognition results.

In real applications, environmental effects such as additive noise and room reverberation lead to a mismatch between training and testing signals that substantially reduces the performance of far-field speaker identification. As a solution to this mismatch problem, in this paper, a new binaural speaker identification system is proposed which employs the well-known equalization-cancelation technique in its structure. The equalization-cancelation algorithm is employed to enhance the input test speech and alleviate the detrimental effects of noise and reverberation in the speaker identification system. The performance of the proposed speaker identification system is compared with unprocessed identification systems and a traditional binaural speaker identification system from the literature. The proposed system is evaluated in both anechoic and reverberant conditions using different types of noise at various azimuthal positions. Simulation results show the superiority of the proposed method in all experimental conditions.

Depression is a widespread mental health problem around the world with a significant burden on economies. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect to achieve that goal is to use technology and monitor depression remotely and relatively inexpensively using automated agents. There has been numerous efforts to automatically assess depression levels using audiovisual features as well as text-analysis of conversational speech transcriptions. However, difficulty in data collection and the limited amounts of data available for research present challenges that are hampering the success of the algorithms. One of the two novel contributions in this paper is to exploit databases from multiple languages for acoustic feature selection. Since a large number of features can be extracted from speech, given the small amounts of training data available, effective data selection is critical for success. Our proposed multi-lingual method was effective at selecting better features than the baseline algorithms, which significantly improved the depression assessment accuracy. The second contribution of the paper is to extract text-based features for depression assessment and use a novel algorithm to fuse the text- and speech-based classifiers which further boosted the performance.

Humanoid robots require to use microphone arrays to acquire speech signals from the human communication partner while suppressing noise, reverberation, and interferences. Unlike many other applications, microphone arrays in humanoid robots have to face the restrictions in size and geometry. To address these challenges, this paper presents an approach to differential beamforming with arbitrary planar array geometries. The major contributions of this work are as follows: (1) a method is presented to design differential beamformers, which works for regular geometries such as linear, circular, and concentric circular ones, as well as irregular geometries, as long as the sensors’ positions are given or can be measured; (2) fundamental requirements for the design of different orders of linear differential microphone arrays (DMAs), partially steerable DMAs, fully steerable DMAs, and robust DMAs are discussed; (3) the validity and limitations of the Jacobi-Anger expansion approximation is analyzed, where we discuss how to achieve an optimal approximation by properly choosing the reference point; and (4) we show how to design an Nth-order DMA with 2N microphones using the Jacobi-Anger expansion.

This paper proposes a direction of arrival estimation based on sparse signal reconstruction in the presence of alpha noise by the off-grid orthogonal matching pursuit algorithm. Assuming Gaussian distribution as the noise model, the previous sparse reconstruction enhances robustness by utilizing the least square criterion-based direction of arrival estimation algorithms. However, they severely degrade, even invalid, when there is thick trailing and large impulse in the noise molded by [Formula: see text] stable distribution. In addition, due to the discretization of the potential angle space, the accuracy of these methods will be reduced when the target is not completely on the divided mesh exactly. Increasing the grid density to improve estimation effect will increase the computation burden. The compressed sensing signal model is reconstructed by reshaping the fractional lower order covariance matrix of the sensor array received signal. Based on the reshaped signal model, the novel reshaped orthogonal matching pursuit algorithm and reshaped off-grid orthogonal matching pursuit algorithm are derived. Compared to the least square criterion method with Gaussian distribution assumption, the proposed algorithm obtains high-resolution direction of arrival estimation in [Formula: see text] noise. Moreover, the reshaped off-grid orthogonal matching pursuit algorithm improves the direction of arrival estimation accuracy with off-grid target. Numerical simulation results demonstrate the effectiveness of proposed method in direction of arrival estimation with off-grid targets in [Formula: see text] noise.

The existence of nonlinearity is an inevitable frequent occurrence that should be considered to accurately identify the modal parameters of a vibration system using operational modal analysis. A problem is that the traditional operational modal analysis method based on the linear modal theory is not applicable to modal parameter identification of vibration systems with nonlinearity. A solution is as follows: this paper is aimed at solving the problem by proposing a new operational modal analysis method to carry out modal parameter identification for a nonlinear vibration system. The new operational modal analysis method, based on the forced response and symbolic regression method without assuming any pre-existing information and only using mathematical symbols, is introduced to solve the problem by automatically searching for the expression structure and modal parameters of a system in nonlinear normal modes. The simulation result of a three-degrees-of-freedom nonlinear system reveals the high accuracy of the proposed operational modal analysis method in extracting the modal parameters. Then, a rod fastening rotor model is considered, and the capability of the proposed operational modal analysis method to precisely extract its modal parameters is further evaluated.

The paper aims at evaluating the sliding mode control parameters used to tune a dynamic damper in the vibration reduction system. The effectiveness of the presented vibration reduction system and the power of the controlled voltage source supplying an electrodynamic linear motor have been analysed. The classical sliding mode control algorithm has been modified based on the analyses of results of calculation. The operation of the modified algorithm has been checked during a simulation. The proposed vibration reduction system was implemented on a laboratory stand. The measurement results have confirmed the effectiveness of the considered system.

It is seen that blisk will be in a nodal diameter vibration state under the engine order excitation, which can be simulated by traveling wave excitation. The nodal diameters rotate on the blisk and it is necessary to identify the specific form of the nodal diameter vibration. Here, based on the determination of nodal diameter vibration parameters, a novel identification method of the nodal diameter vibration was proposed through the experimental test. Specifically, a traveling wave excitation system was built using the LabVIEW control software and noncontact magnetic exciters. Based on the description of resonant state of blisk, the identification principle formulas of the nodal diameter vibration were derived and the identification procedure was given. Taking uncoated and coated blisks as the research objects, the identification method was demonstrated. The identification results showed that the shape, direction, speed, and period of nodal diameter vibration were successfully obtained by the proposed method. In addition, it was found that coatings have a damping effect on blisk. Because of the coating introduction, the blisk vibration response was reduced, the rotation speed of nodal diameter was slowed down, and the rotation period of nodal diameter was increased.

The effects of gender, stature, and body mass on the biodynamic response of supine humans during transport are presented in this work. Twelve female and 14 male subjects were strapped to a litter and spine board and transported in a military ground ambulance that was driven on a track ranging from smooth to bumpy conditions. Female subjects showed more pelvis vertical motion, less pelvis roll, and more pelvis pitch ( p

Binaural sound source localization is an important and widely used perceptually based method and it has been applied to machine learning studies by many researchers based on head-related transfer function (HRTF). Because the HRTF is closely related to human physiological structure, the HRTFs vary between individuals. Related machine learning studies to date tend to focus on binaural localization in reverberant or noisy environments, or in conditions with multiple simultaneously active sound sources. In contrast, mismatched HRTF condition, in which the HRTFs used to generate the training and test sets are different, is rarely studied. This mismatch leads to a degradation of localization performance. A basic solution to this problem is to introduce more data to improve generalization performance, which requires a lot. However, simply increasing the data volume will result in data-inefficiency. In this paper, we propose a data-efficient method based on deep neural network (DNN) and clustering to improve binaural localization performance in the mismatched HRTF condition. Firstly, we analyze the relationship between binaural cues and the sound source localization with a classification DNN. Different HRTFs are used to generate training and test sets, respectively. On this basis, we study the localization performance of DNN model trained by each training set on different test sets. The result shows that the localization performance of the same model on different test sets is different, while the localization performance of different models on the same test set may be similar. The result also shows a clustering trend. Secondly, different HRTFs are divided into several clusters. Finally, the corresponding HRTFs of each cluster center are selected to generate a new training set and to train a more generalized DNN model. The experimental results show that the proposed method achieves better generalization performance than the baseline methods in the mismatched HRTF condition and has almost equal performance to the DNN trained with a large number of HRTFs, which means the proposed method is data-efficient.

Active noise control systems can effectively suppress the impact of low-frequency noise and they have been applied in many fields. Recently, the evolutionary computation algorithm-based active noise control system has attracted considerable attention. To improve the noise reduction performance of the evolutionary computation algorithm-based active noise control system and solve the problem that the system cannot converge again when the path abruptly changes in steady state, we propose the path abruptly change-quantum-behaved particle swarm optimization algorithm. We apply quantum-behaved particle swarm optimization, a global optimization algorithm, to the active noise control system to improve noise reduction performance. In addition, the scheme of detecting the abrupt path change in steady state and performing re-convergence processing is designed to effectively address the problem that the system cannot regain convergence after a path change in steady state. The simulation study demonstrates that the proposed algorithm can efficiently improve noise reduction performance, accurately detect the path change, and re-converge to new global optimization.

With this article we want to offer a critical approach to participatory practices within sound art. To this end, we will analyse the work of two groups of sound artists who have placed participation and collaboration at the centre of their work and of their definition of sound art itself, using two very different approaches: Ultra-red and Escuchatorio. Both groups understand listening as a political action which always implies a relationship with others and with the environment. However, their very different ways of activating the collective listening may encourage us to consider how collaborative art is understood and practised at a time when the interest in participation from different artistic and cultural institutions (also political) keeps growing.By considering Escuchatorio and Ultra-red, we want to ask ourselves how it can be decisive who proposes to perform the sound action: whether it is a community in struggle or a group of artists, or a gallery/institution. Different proposers generate different receptors/participants and also different ways of understanding which values are at stake and how they are distributed. Who is able to participate and how it can be done entail different degrees of involvement, impact and barriers. For a specific group that meets in a specific place participatory art can foreground the differences between people who are close to each other (Ultra-red), whereas the participation of anyone who can use any type of recording device and upload recordings to the network emphasises the similarities between people who are otherwise strangers (the expanded radio of Escuchatorio).

In this paper, we use empirical mode decomposition and Hurst-based mode selection (EMDH) along with deep learning architecture using a convolutional neural network (CNN) to improve the recognition of dysarthric speech. The EMDH speech enhancement technique is used as a preprocessing step to improve the quality of dysarthric speech. Then, the Mel-frequency cepstral coefficients are extracted from the speech processed by EMDH to be used as input features to a CNN-based recognizer. The effectiveness of the proposed EMDH-CNN approach is demonstrated by the results obtained on the Nemours corpus of dysarthric speech. Compared to baseline systems that use Hidden Markov with Gaussian Mixture Models (HMM-GMMs) and a CNN without an enhancement module, the EMDH-CNN system increases the overall accuracy by 20.72% and 9.95%, respectively, using ak-fold cross-validation experimental setup.

In this research, it was investigated if the sound absorption theoretical models for porous materials can be implemented to cementitious materials. The research uses porous concrete panels for the measurements in the reverberation room. The result of the experimental measurements is compared with a theoretical model based on geometrical parameters, to evaluate the possible application in the prediction of the sound-absorbing properties without further measurements.

Text-to-speech (TTS) synthesis systems have been widely used in general-purpose applications based on the generation of speech. Nonetheless, there are some domains, such as storytelling or voice output aid devices, which may also require singing. To enable a corpus-based TTS system to sing, a supplementary singing database should be recorded. This solution, however, might be too costly for eventual singing needs, or even unfeasible if the original speaker is unavailable or unable to sing properly. This work introduces a unit selection-based text-to-speech-and-singing (US-TTS&S) synthesis framework, which integrates speech-to-singing (STS) conversion to enable the generation of both speech and singing from an input text and a score, respectively, using the same neutral speech corpus. The viability of the proposal is evaluated considering three vocal ranges and two tempos on a proof-of-concept implementation using a 2.6-h Spanish neutral speech corpus. The experiments show that challenging STS transformation factors are required to sing beyond the corpus vocal range and/or with notes longer than 150 ms. While score-driven US configurations allow the reduction of pitch-scale factors, time-scale factors are not reduced due to the short length of the spoken vowels. Moreover, in the MUSHRA test, text-driven and score-driven US configurations obtain similar naturalness rates of around 40 for all the analysed scenarios. Although these naturalness scores are far from those of vocaloid, the singing scores of around 60 which were obtained validate that the framework could reasonably address eventual singing needs.

Phonetic information is one of the most essential components of a speech signal, playing an important role for many speech processing tasks. However, it is difficult to integrate phonetic information into speaker verification systems since it occurs primarily at the frame level while speaker characteristics typically reside at the segment level. In deep neural network-based speaker verification, existing methods only apply phonetic information to the frame-wise trained speaker embeddings. To improve this weakness, this paper proposes phonetic adaptation and hybrid multi-task learning and further combines these into c-vector and simplified c-vector architectures. Experiments on National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) 2010 show that the four proposed speaker embeddings achieve better performance than the baseline. The c-vector system performs the best, providing over 30% and 15% relative improvements in equal error rate (EER) for the core-extended and 10 s–10 s conditions, respectively. On the NIST SRE 2016, 2018, and VoxCeleb datasets, the proposed c-vector approach improves the performance even when there is a language mismatch within the training sets or between the training and evaluation sets. Extensive experimental results demonstrate the effectiveness and robustness of the proposed methods.

So-calledfull-face masksare essential for fire fighters to ensure respiratory protection in smoke diving incidents. While such masks are absolutely necessary for protection purposes on one hand, they impair the voice communication of fire fighters drastically on the other hand. For this reason communication systems should be used to amplify the speech and, therefore, to improve the communication quality. This paper gives an overview of communication enhancement techniques for masks based on digital signal processing. The presented communication system picks up the speech signal by a microphone in the mask, enhance it, and play back the amplified signal by loudspeakers located on the outside of such masks. Since breathing noise is also picked up by the microphone, it’s advantageous to recognize and suppress it – especially since breathing noise is very loud (usually much louder than the recorded voice). A voice activity detection distinguishes between side talkers, pause, breathing out, breathing in, and speech. It ensures that only speech components are played back. Due to the fact that the microphone is located close to the loudspeakers, the output signals are coupling back into the microphone and feedback may occur even at moderate gains. This can be reduced by feedback reduction (consisting of cancellation and suppression approaches). To enhance the functionality of the canceler a decorrelation stage can be applied to the enhanced signal before loudspeaker playback. As a consequence of all processing stages, the communication can be improved significantly, as the results of measurements of real-time mask systems show.

Technology that measures bridge responses when a vehicle is crossing over it for structural health monitoring has been under development for approximately a decade. Most of the proposed methods are based on identification of the dynamic characteristics of a bridge such as the natural frequency, the mode shapes, and the damping. Specifically, many time–frequency domain approaches have been used to extract complex spectrum signatures from the complicated vibrations of a bridge due to the interactions of a vehicle with the bridge, which usually involves nonlinear, nonstationary, stochastic, and impact vibrations. In this paper, a method known as complete ensemble empirical mode decomposition with adaptive noise is applied for the first time to analyze the acceleration response of a bridge to a moving vehicle, and the purpose is to extract the spectrum signature of the vehicle–bridge response for structural health monitoring. The time–frequency Hilbert-Huang transform (HHT) spectrum of the decomposed mode from complete ensemble empirical mode decomposition with adaptive noise is presented. The results are well-correlated with finite element analysis. The advantages of the complete ensemble empirical mode decomposition with adaptive noise method are demonstrated in comparing the data from conventional methods, including power spectra, spectrograms, scalograms, and empirical mode decomposition.

This paper presents and evaluates in detail a conjugate-pair decomposition method for tracking the instantaneous frequency of a vibration signal by processing only three (or more) most recent data points, and Probability Box theory was employed to obtain the uncertain probability distribution of instantaneous frequency. A composite cable-stayed bridge with large span, named as Guanhe Bridge, was selected as the engineering background; the proposed conjugate-pair decomposition method is verified and compared with Hilbert–Huang transform method using the measured dynamical data of structural health monitoring system. Moreover, the uncertain probability distribution of instantaneous frequency was discussed; the Probability Box also can be obtained. Results show that the identify results are close to each other using conjugate-pair decomposition and Hilbert–Huang transform, respectively; but the variance of conjugate-pair decomposition is less than Hilbert–Huang transform, and conjugate-pair decomposition requires only a few data points for sliding-window fitting to extract the instantaneous frequency, and the Probability Box of instantaneous frequency will be useful to evaluate safety and potential fatigue problems in the bridge cables.

Multiline spectral vibration (or noise) is very common in rotating machinery like motor, gearbox, compressor, propeller, etc. Active reshaping of these vibrations is one of the most important branches for active vibration control, which may find application in fields of sound quality control, psychoacoustics, military camouflage, etc. The traditional filtered-reference LMS is the most popular structure for multiline spectral reshaping control. However, with the increase of spectral lines, vibration sources and observation points (feedback channels), the traditional structure will have extremely heavy computational complexity, especially for the system whose dynamic of secondary path is complex, since it requires two long filters for every single spectral line and secondary path to adjust the reference signals. In this study, we propose a fast-multiline spectrum reshaping algorithm for active vibration control. We first construct an equivalent system of the traditional multiline spectrum reshaping algorithm by introducing two extra control branches, which is able to improve the convergence property of the system. Then, we generate a group of pre-phase-scheduled reference signals using the phase information at certain frequencies of the secondary paths instead of using filters. Afterwards, we conduct a theoretical analysis of computational complexity of multiline spectrum reshaping and fast multiline spectrum reshaping algorithms. Finally, we carried out two case studies based on the FEMilS test-bed to verify the effectiveness of the proposed algorithm. The results show that the proposed algorithm performs an accurate residual vibration control with uniformed convergence speed and reduced computational complexity.

This paper applies the VIM-Padé technique for solving a nonlinear free vibration of a conservative oscillator. It is a combined method based on the variational iteration method, Laplace transformation and the Padé approximation. An approximated solution with extremely high accuracy can be obtained with ease. Runge-Kutta method is adopted to verify the efficiency of the technique.

In the development of automation industry, external force detection/monitoring is essential for the safety of human and machines. To detect or measure these forces easily, a static loading sensor is required. However, commercially available sensors are too bulky and cause installation difficulties in automated machines. A piezoelectric element is an electro-mechanical component wildly used for actuator sensing or harvesting applications. In this paper, a 10 N capacity thin piezoelectric force sensor is designed and tested for external static load detection. This paper considers that larger strain made by an external force on a proposed piezoelectric sensor generates higher voltage, thereby increasing its force estimation accuracy. Meanwhile, in most piezoelectric force sensors, a cantilever beam mechanism is usually adopted for its simple structure; however, large deformations seen in cantilever piezoelectric sensors are not proportional to its measuring accuracy nor force bearing capacity. Therefore, in this paper, an amplified trapezoidal shape mechanism is designed to convert external vertical load into horizontal displacement deformation in order to obtain higher voltage generation. The Taguchi optimization design method is proposed to analyze the key factors that affect the amplified mechanism design of the piezoelectric sensor. Moreover, static loading force applied on a piezoelectric component generates constant voltage that eventually decreases with time. This is because of its internal electric impedance property, which causes difficulty in static loading force estimation. To represent the theoretical model of the static force estimation in this paper, a chain scattering description matrix of a two-port network was proposed to represent the electric impedance in relation to the mechanical properties’ variation. The measured results demonstrate the accuracy of the proposed theoretical method which implies that the external force can be estimated by measuring its electric impedance variation, particularly the shift in resonance frequency Therefore, based on the developed theoretical model, a driving voltage with fixed frequency can measure its electric current in the same time, thereby achieving the external force monitoring technology.

This paper proposes the visual servoing resolved acceleration control scheme applied to a 3-degree of freedom (DOF) planar manipulator, which aims to reduce computational loads of the feedback control loop and the tracking errors of the end-effector in the conventional resolved acceleration control scheme. The computation loads are increased due to the calculations of the direct kinematics and the velocity inversion in the feedback control loop. The tracking errors of the end-effector are increased due to low-accuracy encoders, gearbox backlashes, and manipulator flexibility, etc. The proposed scheme incorporates a visual system, which directly captures the position of the end-effector instead of calculating the direct kinematics and the velocity inversion. Due to the requirement of inverse dynamics in the proposed scheme, the transformation of the Jacobian matrices between the passive and active links is imposed to the Euler-Lagrange’s equation so as to derive dynamic equations. In order to show the control performances, three existing torque-based control schemes are also applied to the manipulator. This study investigates the numerical simulations and the experimental validations, and the results show that the proposed scheme can reduce the computation loads and the tracking errors of the end-effector to compare with the three existing schemes.

An unbalanced propeller can affect a quadcopter’s performance due to vibration, also decreasing its thrust’s yield. Analyses were conducted to determine the correlation between vibration in the dynamic movement of a balanced and an imbalanced propeller using a laser vibrometer (portable digital vibrometer) PDV-100, to determine the correlation between propeller rotational speed and its vibration, as well as to determine the propeller’s maximum rotational speed to avoid over-vibration. The vibration analysis was conducted on a carbon fiber 2-blade propeller by comparing the results of vibration tests with the propeller blade contour. The vibration response of the propeller has been analyzed at three points, respectively the hub, the center, and the tip of the blade, to determine the point having the largest value of the vibration on the propeller running at a maximum rotation speed of 7000 rpm. The same analysis was made on two propellers: the first one of type 1340 with a diameter of 13 inches and the second one of type 1447 with a diameter of 14 inches. The vibration was reduced by propeller’s static balancing, thus increasing the propeller stability. The result showed that an imbalanced propeller generated a decrease in the rotational speed and higher vibration values compared to the balanced propeller. The vibration values showed a linear dependency to the rotor speed; the higher the speed, the bigger the vibration. The limit of rotational speed for the balanced model of the 1340 type propeller was 5000 rpm whilst the corresponding value for the 1447 type propeller was 4500 rpm. Finally, the result was used to optimize the propeller’s overall performance.

This paper presents a numerical approach for the determination of scattering frequencies of two-dimensional acoustic exterior resonance problems using boundary element method combined with a contour integral method. Since the solving range is surrounded by a Jordan curve in the complex plane, the complex eigenvalues can be acquired through evaluating the contour integral along the Jordan curve using the contour integral method. Complex scattering frequencies are directly extracted by the proposed approach, and their corresponding eigenmodes are also presented by evaluating the vibration amplitudes of internal points. The behaviors of the fictitious eigenfrequencies for unbounded domain are also studied. Numerical examples demonstrate the effectiveness of the approach for the resonance problems of unbounded domains.

A method called joint connectionist temporal classification (CTC)-attention-based speech recognition has recently received increasing focus and has achieved impressive performance. A hybrid end-to-end architecture that adds an extra CTC loss to the attention-based model could force extra restrictions on alignments. To explore better the end-to-end models, we propose improvements to the feature extraction and attention mechanism. First, we introduce a joint model trained with nonnegative matrix factorization (NMF)-based high-level features. Then, we put forward a hybrid attention mechanism by incorporating multi-head attentions and calculating attention scores over multi-level outputs. Experiments on TIMIT indicate that the new method achieves state-of-the-art performance with our best model. Experiments on WSJ show that our method exhibits a word error rate (WER) that is only 0.2% worse in absolute value than the best referenced method, which is trained on a much larger dataset, and it beats all present end-to-end methods. Further experiments on LibriSpeech show that our method is also comparable to the state-of-the-art end-to-end system in WER.

This paper investigates the stochastic P-bifurcation behavior of bistability in a generalized Van der Pol system with fractional time-delay feedback under additive and multiplicative Gaussian white noise excitations. First, using the minimal mean square error principle, the fractional derivative is found to be equivalent to a linear combination of damping and restoring forces, and the original system is transformed into an equivalent integer-order system. Second, the stationary probability density function of system amplitude is obtained by stochastic averaging, and according to singularity theory, the critical parameters for stochastic P-bifurcation of the system are found. Finally, the different types of stationary probability density function curves of the system amplitude are qualitatively analyzed by choosing the corresponding parameters in each region divided by the transition set curves. The consistency between the analytical solutions and the Monte Carlo simulation results verifies the theoretical analysis in this paper. The method used in this paper can directly guide the design of the fractional-order controller to adjust the response of the system.

The rapid development of technology is causing the replacement of many traditional manufacture industries by automation. Robotic arms are now commonly used in many sectors. The requirements for robotic arms in different sectors are quite different; but, in general, robotic arms not only save cost and man power, they also improve safety. The aim of this study was an investigation of the integration of image identification with robotic arms. To do this, the Denavit–Hartenberg transformation matrix was used to analyze the mechanical kinematics of the joints of each robotic arm axis. This allowed the spatial relationships between the Cartesian 3 D coordinate system and the joint of each axis to be determined and communication between the robotic arm and image identification to be established. A robotic arm prototype platform with automatic image identification and calibration was constructed using a quick and robust method. Several of the variables that exist in real robotic arm applications have been solved in this study: primarily the accuracy error that occurs when repeated gripping of workpieces is done and a movement and placement track is set up. Deviations occur frequently and if image identification can be applied to offset repeated inaccuracy, the quality of finished products will be more consistent. The Universal Robots 5 six-axis robot and cameras, which use a filter, Sobel computation, and Hough transformation computer vision image processing technology, together comprise a system that can automatically identify the workpiece and carry out loading and unloading accurately.

Aviation axial piston pump is an important energy conversion component in aircraft hydraulic system, and the study on the nonlinearity of key operating mechanism is very necessary for improving the reliable operation of the pump. Addressing the aviation axial piston pump, this article analyzes nonlinear dynamic characteristics of the center spring return mechanism by using theoretical analysis and numerical simulation. Firstly, mathematical models of oil-film forces of slipper/swash plate pair and cylinder/port plate pair are derived. Then, based on the nonlinear dynamic theory, some specific theoretical analysis methods such as Lyapunov index spectrum, bifurcation diagram, time series, phase plane portrait, Poincaré map, and power spectrum are used to carry out the numerical simulation of nonlinear dynamic behaviors. The result indicates that characteristics of the center spring can reveal the reason for the nonlinear vibration of the center spring return mechanism. Meanwhile, the behavior of the oil thickness of slipper/swash plate pair is in the periodic and steady motion, and the variation of the oil thickness of cylinder/port plate pair is in chaotic vibration, which establishes the theoretical foundation of the aviation axial piston pump for the traceability and suppression of vibration. The obtained results are of important guidance significance for optimal design and dynamic control of the center spring return mechanism of aviation axial piston pump.

In this paper, the dynamic response of a piecewise linear single-degree-of-freedom oscillator with fractional-order derivative is studied. First, a mathematical model of the single-degree-of-freedom system is established, and the approximate steady-state solution associated with the amplitude–frequency equation is obtained based on the averaging method. Then, the amplitude–frequency response equations are used for stability analysis, and the stability condition is founded. To validate the correctness and precision, the approximate solutions determined by the analytical method are compared with the solutions based on the numerical integration method. It is found that the approximate solutions and the numerical solutions are in excellent agreement. Finally, the effects of system parameters, such as fractional-order coefficient, order, clearance, and piecewise stiffness, on the complex dynamical behaviors of the piecewise linear single-degree-of-freedom oscillator are studied. The results show that the system parameters not only influence resonance amplitude and resonance frequency but also affect the size of the unstable region.

In this study, vibration control was achieved using macro fiber composite actuators initiated by feedback from polyvinylidene difluoride (PVDF) membrane sensors. First, the structure mode was found using the finite element method after which simulation of the effect of piezoelectric actuators on the structure as well as a feasibility investigation was carried out. The ANSYS Workbench was used for parametric design and to determine the best position for the actuators. Structure vibration in different modes was measured using PVDF membrane sensors for feedback control and to investigate the vibration reduction effect achieved with three individual controllers. The pros and cons of active vibration reduction control were also examined in several different modes. The experimental results obtained with several types of vibration reduction control were compared: Type-1 fuzzy control, Type-1 adaptive fuzzy control, and the vibration reduction effect of an adaptive neuro-fuzzy inference system. The results showed that the adaptive neuro-fuzzy inference system controller was the best choice under the experimental conditions used in this study.

[Abstract] Search on speech (SoS) is a challenging area due to the huge amount of information stored in audio and video repositories. Spoken term detection (STD) is an SoS-related task aiming to retrieve data from a speech repository given a textual representation of a search term (which can include one or more words). This paper presents a multi-domain internationally open evaluation for STD in Spanish. The evaluation has been designed carefully so that several analyses of the main results can be carried out. The evaluation task aims at retrieving the speech files that contain the terms, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: the MAVIR database, which comprises a set of talks from workshops; the RTVE database, which includes broadcast news programs; and the COREMAH database, which contains 2-people spontaneous speech conversations about different topics. We present the evaluation itself, the three databases, the evaluation metric, the systems submitted to the evaluation, the results, and detailed post-evaluation analyses based on some term properties (within-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and native/foreign terms). Fusion results of the primary systems submitted to the evaluation are also presented. Three different research groups took part in the evaluation, and 11 different systems were submitted. The obtained results suggest that the STD task is still in progress and performance is highly sensitive to changes in the data domain. Ministerio de Economía y Competitividad; TIN2015-64282-R, Ministerio de Economía y Competitividad; RTI2018-093336-B-C22 Ministerio de Economía y Competitividad; TEC2015-65345-P Xunta de Galicia; ED431B 2016/035 Xunta de Galicia; GPC ED431B 2019/003 Xunta de Galicia; GRC 2014/024 Xunta de Galicia; ED431G/01 Xunta de Galicia; ED431G/04 Agrupación estratéxica consolidada; GIU16/68 Ministerio de Economía y Competitividad; TEC2015-68172-C2-1-P

A stainless-steel cantilever multistage centrifugal pump was taken to study the transient response characteristic. The axial locus under different working conditions was tested experimentally and compared in detail. The results show that the gyroscopic effect increases the radial displacement located near the end element, and the amplitude of the radial displacement of the pump body increases with the development of unbalance mass. The dynamic sealing force of wear-ring could enhance the stability of the rotor system, and effectively reduce the amplitude of the radial displacement. The axis locus under variable flow rates is different when the fluid excitation was loaded. The increasing tendency of the maximal radial displacement appears exponential with the enhancement of fluid added mass when heterogeneous fluid added mass is taken into consideration. The experimental results exhibit that the axis locus is various under different working conditions, and there exists misalignment and eccentricity phenomenon in the multi-stage pump rotor system.

Harmonic vibration synchronization of the two excited motors is an important factor affecting the performance of the nonlinear vibration system driven by the two excited motors. From the point of view of the hysteresis force, the nonlinear dynamic models of the nonlinear vibration system driven by the two excited motors are presented for the analysis of the hysteresis force with the asymmetry. An approximate periodic solution for the nonlinear vibration system with the hysteresis force is investigated using the nonlinear models. The condition of harmonic vibration synchronization is theoretically analyzed using the rotor–rotation equations of the two excited motors in the nonlinear dynamic models and the stability condition of harmonic vibration synchronization also is theoretically analyzed using Jacobi matrix of the phase difference equation of two excited motors. Using Matlab/Simlink, harmonic vibration synchronization of the two excited motors and the stability of harmonic vibration synchronization for the nonlinear vibration system with the hysteresis force are analyzed through the selected parameters. Various synchronous processes of the nonlinear vibration system with the hysteresis force are obtained through the difference rates of the two excited motors (including the initial phase difference, the initial rotational speed difference, the difference of the motors parameters). It has been shown that the research results can provide theoretical basis for the design and research of the vibration system driven by the two-excited motors.

L'obiettivo di questa tesi è¨di contribuire all'identificazione di strumenti e requisiti per l'analisi acustica della voce come misura oggettiva di prevenzione e valutazione della disfonia. La prima parte comprende studi sulle distribuzioni di parametri sul carico vocale attraverso monitoraggi degli insegnanti ed esperimenti in laboratorio sugli effetti della riverberazione nell'intensità del parlato. La seconda parte è¨ dedicata alla valutazione della salute vocale: distribuzioni individuali di parametri cepstrali sono stati studiati utilizzando diversi sensori per l'acquisizione del segnale vocale. L'incertezza di ciascun parametro è¨ stata valutata con esperimenti adeguati. - The overall aim of this work is to contribute to the identification of tools and requirements related to voice acoustic analysis as objective measure to prevent and assess dysphonia. The first part includes studies on individual distributions of vocal-load parameters, i.e, an in-field study of teachers’ voice use and in-lab experiments on the effects of reverberation in speech intensity. The second part is addressed to vocal health assessment: individual distributions of Cepstral Peak Prominence Smoothed from patients and controls were investigated in different speech materials and devices. The uncertainty of each parameter has been evaluated with proper experiments.

La camera riverberante è un importante strumento di ricerca in ambito acustico, utilizzato nello svolgimento di numerose misure standardizzate. Tali procedure, basate sul concetto di campo sonoro diffuso, richiedono di verificare l'doneità dello spazio di misura. Il mancato rispetto dei limiti imposti dalla norma [1], ha portato allo svolgimento di una duplice analisi: la prima riguarda una nuova metodica sperimentale per lo studio del decadimento sonoro a bassa frequenza, tramite la post elaborazione della risposta all'impulso; la seconda riguarda l'analisi numerica al FEM della camera riverberante. Differenti configurazioni di misura vengono studiate con lo scopo di valutarne l'effetto sulla diffusività del campo sonoro. - Reverberant chamber is an important research instrument in the acoustic field, mainly used to perform standardized measurements. Based on the concept of diffuse sound field, these procedures require to verify the suitability of the measurement space. The failure to respect the limits imposed by the standard [1], led to a double analysis of the reverberant sound field: the first is about a new experimental method for the study of low-frequency sound decay, through post-processing of the measured impulse response; the second concerns the numerical simulations of the reverberant chamber. Different measurement configurations are studied in order to evaluate their effect on the sound field diffusivity.

Un sistema audio-immersivo permette di creare delle sorgenti audio virtuali che non esistono nell’ambiente fisico reale. In questo articolo viene implementato un sistema multibanda in grado di riprodurre una sensazione di immersività, sfruttando un array lineare di altoparlanti. Il sistema è basato su un algoritmo di beamforming, un algoritmo di cancellazione del crosstalk e un processo di equalizzazione. Ѐ stata realizzata un’implementazione real-time di tale sistema attraverso l’utilizzo della piattaforma NU-Tech per valutare le prestazioni dell’algoritmo attraverso dei test oggettivi e soggettivi. I risultati ottenuti hanno dimostrato una buona efficacia del sistema implementato. - An audio-immersive system allows to create virtual audio sources that don’t exist in the real physical environment. In this paper, a multiband system able to reproduce an immersion sensation, exploiting a linear array of loudspeakers. The system is based on a beamforming algorithm, a crosstalk elimination algorithm and an equalization process. Therefore, an implementation of this system is proposed using the NU-Tech framework, in order to evaluate algorithm performances through objective and subjective tests. The obtained results have been proved a good effectiveness of the implemented system.

Nella società contemporanea le sale di ristorazione diventano luoghi di convivialità e comunicazione. In tale ambito risulta di significativo interesse lo studio del fenomeno acustico denominato cocktail party effect. Nel presente lavoro sono stati valutati l'indice di trasmissione del parlato e la differenza di livello sonoro con metodi analitici e simulazioni software al fine di ricercare, nelle sale di ristorazione, correlazioni tra tali indicatori acustici e il cocktail party effect. - In contemporary society restaurant dining rooms become places of conviviality and communication. In this context the study of the acoustical phenomenon called cocktail party effect is a major issue. In the present work the speech transmission index and the sound level difference have been assessed through analytical methods and software simulations in order to find, in restaurants, correlations between these acoustic indicators and the cocktail party effect.

© The Author(s) 2019. Hermetic digital scroll compressor has been widely used as a small-scale organic Rankine cycle application in the heating, ventilation, and air conditioning systems. A clunking noise issue is recently found in an air conditioning outdoor unit, and the main cause of the noise is experimentally identified to be the impact of the scrolls in the compressor unit during the switching process. The semi-active control methods are thus designed to greatly reduce the noise level by using additional valves to adjust the pressure changing rate within the modulation chamber. The response time for the impact of the scrolls can then be controlled by the added valves. The additional release valve with a smaller diameter pipe parallel to the main valve is tested firstly for its performance. Slower flow rate is produced and the pipe can extend the response time and decrease the speed of the impact process by reducing the pressure changing rate. The use of a discharge valve is also tested for controlling the pressure changing rate inside the chamber. The discharge valve with an opposite effect to the release valve is found useful for solving the noise issue. Both noise and vibration results confirm that the impact noise in the frequency range of interest can be reduced by using the proposed semi-active control methods.

Ball bearings are important parts of all modern rotating machines. Their function is to reduce friction, support rotating shafts and spindles, and bear loads. Bearing damage can result in abnormal vibrations, cause machine malfunction, and even be dangerous. In this study, analysis of four different ball-bearing conditions was carried out: normal bearings and bearings with inner ring, rolling body, and outer ring malfunction. This was based on electromechanical vibration signals produced on a fault diagnosis simulation platform. The objective was to use a series of signal processing analytical methods to build a set of identification models used to forecast malfunction. Wavelet packet transform technology was first used to process the vibration signal. The signals were pre-processed and analyzed before eigenvalue calculation was done to analyze the signal changes which allowed determination of the nature of the bearing malfunction to be made. The extracted eigenvalues and ball-bearing status categories were input to the support vector machine for model training and testing. Finally, the constructed model parameters were integrated with particle swarm optimization, and the artificial fish-swarm algorithm was used to obtain the optimal parameters for the classifier, and this improved the accuracy of malfunction classification.

It is well known that the sudden unbalance is harmful for rotor systems, since it is in huge amount and has a strong impact. Thus, it is difficult to identify the unbalance from the sudden unbalance response of rotor and very few studies have been done on this topic. This paper focuses on the transient response characteristics of the speed-varying rotor when the sudden unbalance occurs, and the inherent correlation between the unbalance and the transient characteristic quantity. First, different time-dependent functions modelling the loading process of sudden unbalance are derived and compared with the experiment results of the test rotor. The selection principles of function type and duration value are presented. Analyses of transient response characteristics are conducted over the run-up process of the rotor with different parameters of sudden unbalance. Variations of transient response characteristics are summarized into three forms. The varying laws and interrelations of characteristic quantities, and the corresponding parameter region of sudden unbalance are illustrated for each form. Based on the transient analysis results, the momentary positions and angular variations of the rotor disc at some key time instants are illustrated via introducing the key-phase signal. A novel transient-vibration-based method for unbalance identification is proposed. Only a single run-up measurement is required. Numerical experiments verify the effectiveness of the method. In summary, the proposed method can provide a new way for the unbalance identification using the transient response.