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Environmental sound classification is one of the important issues in the audio recognition field. Compared with structured sounds such as speech and music, the time–frequency structure of environmental sounds is more complicated. In order to learn time and frequency features from Log-Mel spectrogram more effectively, a temporal-frequency attention based convolutional neural network model (TFCNN) is proposed in this paper. Firstly, an experiment that is used as motivation in proposed method is designed to verify the effect of a specific frequency band in the spectrogram on model classification. Secondly, two new attention mechanisms, temporal attention mechanism and frequency attention mechanism, are proposed. These mechanisms can focus on key frequency bands and semantic related time frames on the spectrogram to reduce the influence of background noise and irrelevant frequency bands. Then, a feature information complementarity is formed by combining these mechanisms to more accurately capture the critical time–frequency features. In such a way, the representation ability of the network model can be greatly improved. Finally, experiments on two public data sets, UrbanSound 8 K and ESC-50, demonstrate the effectiveness of the proposed method.

The article describes algorithms for the retrieval of linear frequency modulated (LFM) pulses from a received signal which is corrupted by noise and multipath reflections from ground and stationary objects. Such a problem appears, for example, in electronic intelligence (ELINT) or spectrum sensors that receive and analyze signals originating from an active radar emitting LFM signals. Two computationally efficient methods to solve this problem are presented in this paper. The first is a version of time-frequency filtering based on spectrogram zeros. The second method is based on the chirp rate estimation, fractional Fourier Transform (FrFT) and band-pass filtering to retrieve the original pulse signal. Both of the techniques presented were tested using synthetic signals to measure their statistical properties and then positively verified on real-life recorded signals originating from a non-cooperative radar.

The perplexing acoustical properties of a landscape architecture feature of Academy Park on the Purdue University campus have long been the subject of speculation. The feature, known informally as the “Clapping Circle,” consists of sixty-six concentric rings of stone tiles. When someone claps while at the middle of the circle, they hear a high-pitched squeak immediately afterwards. Experiments were conducted by the Purdue student chapter of the Acoustical Society of America to characterize this effect. The response to a clap played from an omnidirectional speaker placed at the center of the circle was recorded using a microphone positioned above the loudspeaker. Spectrograms of the recorded responses revealed the squeak to consist of a descending tone and its harmonics. This tone disappeared from the spectrogram when the tiles were covered with absorbing blankets. A model based on scattering from the bevels between the tile rings reproduced the descending frequency of the squeak. Similarly to famous stepped structures with notable acoustics, the tiles were found to scatter sound best when the wavelength was not larger than the tiles' spatial period. Thus, it was concluded that the squeak is an example of an acoustical diffraction grating which creates a repetition pitch caused by scattering from the tile formation. © 2021 Institute of Noise Control Engineering.

Underwater acoustic target recognition based on ship-radiated noise is difficult owing to the complex marine environment and the interference by multiple targets. As an important technology for target recognition, deep-learning has high accuracy but poor interpretability. In this study, an attention-based neural network (ABNN) is proposed for target recognition in the pressure spectrogram with multi-source interference using an attention module to inspect the inner workings of the neural network. From data obtained during a September 2020 sea trial, the ABNN exhibited a gradual focus on the frequency-domain feature of the target ship and suppressed environmental noises and marine vessel interference, which led to high accuracy in the target detection and recognition.

Kylie is a solitary common dolphin who inhabits a restricted area within the Firth of Clyde (Scotland). She spends most of her time around navigational buoys in the Hunterston/Fairlie channel, where she has been seen interacting with harbour porpoises. Recordings from 2016 and 2017 were used to study her acoustic behaviour when seen alone and with a porpoise. Clicks were classified as potential porpoise or dolphin clicks based on the waveform, power spectrum, and spectrogram, as well as direction of arrival, inter-click interval, amplitude, and centroid frequency variations. Kylie emitted clicks exclusively, which were of variable nature, including low, mid, and high-frequency (HF, centroid frequency > 100 kHz) as well as broad or narrowband. Some of Kylie’s HF clicks were similar to porpoise clicks both in the time (e.g. polycyclic) and frequency (e.g. narrowband with most energy between 100 and 150 kHz) domains, which cannot fully be explained by recording geometry and directionality effects.

Objective To characterize the properties of the audible tones produced by current left ventricular assist device (LVAD) pumps approved for use, and to ascertain if changes in those may be present in the setting of pump thrombosis. Patients and Methods From August 31, 2016, to January 16, 2020, LVAD recipients consented to have surface recordings obtained using a high-fidelity digital stethoscope. Audio data were analyzed using digital recording and editing software to produce an acoustic spectrogram by Fast Fourier transformation. Results Recordings were obtained in 53 patient encounters (27 HeartMate II, 19 HeartWare and 7 HeartMate 3). In 12 patients (9 HeartMate II, 3 HeartWare) there was a clinical concern for pump thrombosis. In all patients and pump models, a fundamental frequency was noted, and the second and third harmonics were also clearly detectable. Where thrombosis occurred in the HeartMate II pump, the absolute (normal -46.9 [-57.5,-42.9] dB vs thrombosis -41.4 [-49.8,-26.8] dB; P=.08) and relative (normal 0.72 [0.62, 0.92] vs thrombosis 0.95 [0.86, 1.24]; P=.01) third harmonic frequencies were increased in amplitude. Where paired data were available, an increase in the absolute and relative third harmonic frequencies was observed in all patients. In the case of the HeartWare device, a consistent difference in harmonic amplitudes in the setting of thrombosis could not be identified. Conclusion A consistent pattern of fundamental and harmonic frequencies is common to all LVADs currently approved for use. Alterations in the amplitude of higher order harmonics may signal the onset of pump thrombosis in axial flow LVADs.

In operating a wind turbine, both predictive and condition-based maintenances are required to minimize the downtime caused by maintenance. The imbalance of rotor rotational speed is an important factor for diagnosing wind turbine failures. The rotational speed imbalance can be caused by accumulated damage or the accumulation of ice, dust, and moisture. In this paper, we proposed a method for detecting the rotational speed imbalance of a wind turbine using a Doppler radar. We calculated the difference in the rotational speed for different times using spectrograms obtained by observing the wind turbine with a Doppler radar and determined the rotational speed imbalance using the fast Fourier transform. The performance of the proposed algorithm was verified using both synthetic and numerical data.

Richtmyer-Meshkov instability (RMI) experiments offer a powerful, new tool for investigating materials strength at extreme strain rates, but obtaining peak velocities from the photon Doppler velocimetry (PDV) results of such tests is often subjective and time consuming. A new, semi-automated program, SAVER, was developed to address these concerns and was tested on data sets from previous RMI studies. Extreme surface velocities extracted using SAVER agreed well with human-determined values, and peak spike velocities – a key RMI metric – were reproduced to within ~ 8% in all cases. A re-analysis of materials strength based on these new SAVER values resulted in an estimated increase of ~ 20%, but no objective measure exists at this time to verify this result. While SAVER proved robust with regards to spectrogram quality, scatter in extracted velocities was seen to increase with decreasing signal quality – emphasizing the importance of PDV instrumentation and processing.

In this study, a new diagnostic system is developed to easily identify bone fractures in non-medical environments. It is difficult to determine the extent of cracks, fractures, and the healing process inside humans owing to the differences among people and limitations of state-of-the-art medical devices. Thus, various medical techniques, such as X-ray, computed tomography, or fork tuning systems have been developed, and more advanced technologies are emerging in the medical engineering field. In hazardous circumstances, medical devices to detect bone fracture are not available or cannot be easily applied. Thus, there is a need for the rapid detection of bone fractures without medical devices. To this end, this study analyzes the transverse vibration responses of bones because bone fractures cause different mechanical vibration reactions. By comparing the transverse vibration responses of both healthy and fractured bones, the modal assurance criterion can be calculated and applied to detect the existence of bone fractures. The transverse vibration responses at low and high frequencies are different and exhibit different modal assurance criteria depending on whether or not they are abnormal. Then, the virtual spectrogram of the differences between the signals from non-fractured and fractured bones is calculated. With the help of the present criterion with transverse vibration data, this difference can be analyzed quantitatively and effectively. To validate the proposed system, experiments with artificial specimens, animal legs, and a cadaver are performed.

The sensitivity of underwater propagation models to acoustic and environmental variability increases with the signal frequency; therefore, realizing accurate acoustic propagation predictions is difficult. Owing to this mismatch between the model and actual scenarios, achieving high-frequency source localization using model-based methods is generally difficult. To address this issue, we propose a deep learning approach trained on real data. In this study, we focused on depth estimation. Several 18-layer residual neural networks were trained on a normalized log-scaled spectrogram that was measured using a single hydrophone. The algorithm was evaluated using measured data transmitted from the linear frequency modulation chirp probe (11-31 kHz) in the shallow-water acoustic variability experiment 2015. The signal was received through two vertical line arrays (VLAs). The proposed method was applied to all 16 sensors of the VLA to determine the estimation performance with respect to the receiver depth. Furthermore, frequency-difference matched field processing was applied to the experimental data for comparison. The results indicate that ResNet can determine complicated features of high-frequency signals and predict depths, regardless of the receiver depth, while exhibiting robust environmental and positional variability.

This paper proposes a new non-stationary three-dimensional (3D) channel model for a physical millimeter wave (mm-Wave) multiple-input multiple-output (MIMO) channel. This MIMO channel model is driven by the trajectory of a moving point scatterer, which allows us to investigate the impact of a single moving point scatterer on the propagation characteristics in an indoor environment. Starting from the time-variant (TV) channel transfer function, the temporal behavior of the proposed non-stationary channel model has been analyzed by studying the TV micro-Doppler characteristics and the TV mean Doppler shift. The proposed channel model has been validated by measurements performed in an indoor environment using a MIMO radar kit operating at 24 GHz. For the measurement campaign, we used a single swinging pendulum as a model for a moving point scatterer. The trajectory of the pendulum has been captured by an inertial measurement unit attached to the pendulum and by a motion capture camera system. The measured trajectories are fed into the proposed mm-Wave MIMO channel model. The results obtained for the micro-Doppler characteristics show an excellent agreement between the proposed MIMO channel model and real-world measured channels in the presence of a moving point scatterer. We believe that our model can serve as a basis for the development of novel non-stationary MIMO channel models capturing the effects caused by moving objects and people.

The ultrasonic fatigue testing (USFT) is an effective method for rapid determination of the fatigue properties of structural materials under high cycle (≥106 cycles) loading. However, the occurrence and accumulation of fatigue damage with this test method remain uncertain due to the limitations of the existing measurement methods. Currently used monitoring methods allow detecting the fatigue cracks, but only in the late stages of failure. Despite the superior sensitivity to localized processes in materials, the use of the acoustic emission (AE) method in ultrasonic testing is extremely difficult due to the presence of resonant noise. This work aimed to suppress resonant noise and extract the signal for early detection of fatigue damage. The authors tested the samples of the AlSi9Cu3 aluminum alloy under the asymmetric cyclic loading (R=0.1) at a resonant frequency of 19.5 kHz with a non-threshold AE registration. The fracture surfaces were analyzed by electron and optical microscopy. The authors processed AE by two different methods: (1) the digital filtering method consisted of detecting resonant noise and removing it from the spectrum; (2) the φ-function method consisted of differentiating the spectrogram by time. The processed spectrograms were integrated by the frequency with further extraction of the AE events using the threshold method. The digital filtering method revealed a correlation between AE signals and fatigue damage, whereas the undamaged control sample showed no signals. The φ-function technique demonstrated ambiguous results, showing high AE activity on the control sample.

The importance of detecting whether a per- son wears a face mask while speaking has tremendously increased since the outbreak of SARS-CoV-2 (COVID-19), as wearing a mask can help to reduce the spread of the virus and mitigate the public health crisis. Besides affecting human speech characteristics related to frequency, face masks cause temporal interferences in speech, altering the pace, rhythm, and pronunciation speed. In this regard, this paper presents two effective neural network models to detect surgical masks from audio. The proposed architectures are both based on Convolutional Neural Net- works (CNNs), chosen as an optimal approach for the spatial processing of the audio signals. One architecture applies a Long Short-Term Memory (LSTM) network to model the time-dependencies. Through an additional attention mechanism, the LSTM-based architecture enables the extraction of more salient temporal information. The other architecture (named ConvTx) retrieves the relative position of a sequence through the positional encoder of a transformer module. In order to assess to which extent both architectures can complement each other when modelling temporal dynamics, we also explore the combination of LSTM and Transformers in three hybrid models. Finally, we also investigate whether data augmentation techniques, such as, using transitions between audio frames and considering gender-dependent frameworks might impact the performance of the proposed architectures. Our experimental results show that one of the hybrid models achieves the best performance, surpassing existing state-of-the-art results for the task at hand.

The interference between the direct path and the sea surface reflection of a signal as measured by a receiver is called Lloyd's Mirror effect (LME). It results in a frequency-dependent interference pattern that can be observed in a spectrogram. LME depends on the receiver depth, signal source depth, signal frequency, and slant range between source and receiver. Knowing three of these parameters a priori, LME can be used to estimate the third parameter, such as source depth. Here, the work in Pereira et al. (2016) was expanded to estimate the depth of a vocalizing fin whale recorded by an ocean-bottom seismometer (OBS). In Pereira et al. (2016), the depth of a vocalizing fin whale was inferred by manually comparing spectrograms of LME transmission loss models with observed LME. This study developed an automated procedure to perform the same task using the LME interference pattern observed in the spectrograms of the hydrophone and the vertical channel of the OBS. The results show that the joint use of the two channels was the best approach to estimate a source depth using LME. LME provides a non-intrusive approach for estimating the depth at which a fin whale was vocalizing.

Radar signatures contributed by rotating blades of drones usually refer to the kinematical micro-Doppler phenomenon on spectrograms. Here, theoretical analysis indicates that when sampling time is short enough that this signature becomes instant micro-Doppler signatures with scattering characteristics, appearing as “blade flash” in the time domain and rotor blade modulation (RBM) feature in the spectrum. In an anechoic chamber, we track this instant micro-Doppler signature by using a dynamic measurement system composed of a network analyzer and a high-speed camera to collect high-resolution range profile of a consumer drone, DJI Phantom 3 Vision within S-band, X-band, and Ku-band. The experimental results demonstrate that rotating blades do cause signal fluctuation of “blade flash” and register the RBM feature in the spectrum, and the modulation effects are weak within S-band, strong within X-band, and stronger within Ku-band. This instant micro-Doppler with scattering characteristic establishes a mapping between radar signature and the rotation structure component of the drone and enables us to distinguish a drone from other nonblades objects in the air, especially with little radar observation time.

Monitoring vessel traffic in coastal regions is a key element of maritime security. For this reason, additional ways of detecting moving vessels are explored by using the unique structure of their wake waves based on pressure measurements at the seabed. The experiments are performed at a distance of about 2 km from the sailing line using novel multisensor devices called “hydromasts” that track both pressure and near-bed water flow current velocities. The main tool for the analysis is a windowed Fourier transform that produces a spectrogram of the wake structure. It is shown that time series from the pressure sensors, measured at a frequency of 100 Hz, 0.2 m above the seabed are a valid source of input data for the spectrogram technique. This technique portrays the properties of both divergent and transverse waves with an accuracy and resolution that is sufficient for the evaluation of the speed and distance of the detected vessels from the measurement device. All the detected passings are matched with vessels using automatic identification system (AIS) data. The use of several time series from synchronized multisensor systems substantially suppresses noise and improves the quality of the outcome compared to one-point measurements. Additional information about variations in the water flow in wakes provides a simple and reasonably accurate tool for rapid detection of ship passages.

Summary Objective Based on simultaneous voice and electroglottograph (EGG) signals, to gain a better understanding of human voice production process, to make pitch-synchronous segmentation of voice signals, and to make visual representations of pitch marks and timbre spectra with high resolution. Methods/Design The traditional spectrogram segments the voice signals with a process window of fixed size and fixed shift, then performs fast Fourier transformation after multiplied with a window function, typically a Hamming window. Then display power spectrum in both frequency and time. Pitch information and timbre information are mixed. The new design segments the signals into pitch periods, either using the derivatives of the EGG signals or based on the voice signals, then performs Fourier analysis to the segment of signals in each pitch period without using a window function. The pitch information and the timbre information are cleanly separated. The graphical representations of both pitch marks and timbre spectra exhibit high resolution and high accuracy. Results Detailed analysis of simultaneously acquired voice and EGG signals provides a more precise understanding of human-voice production process. The transient theory of voice production, proposed by Leonhard Euler in early 18th century, is substantiated with modern data. Based on the transient theory of voice production, a pitch-synchronous spectrogram software is developed, which makes a visual representation of pitch marks and timbre spectra. In addition, the timbre spectrum and the power evolution pattern in each pitch period can be displayed individually. Conclusions Simultaneously acquired voice and EGG signals indicates that each glottal closing triggers a decaying elementary wave in the vocal tract. A superposition of those elementary waves constitutes voice. Based on that concept and using EGG data, a pitch-synchronous voice signal processing method is developed. The voice signal is first segmented into pitch periods, then the two ends are equalized. Fourier analysis is applied to obtain the timbre spectrum of each pitch period. High resolution display of timbre spectrum is generated. The power evolution pattern in each pitch period is also displayed.

This paper investigates the evolution of spectral properties observed in Cuvier's beaked whale (Ziphius cavirostris) click trains recorded by fixed hydrophones in the Gulf of Mexico. In the context of deep water and high-frequency sounds and observed inter-click intervals, the authors assumed that the main effect responsible for the modification of the spectral content between adjacent clicks in the same click train is the source beam pattern. The spectral structure is studied by using the Wigner-Ville time-frequency distribution and is compared with the conventional Fourier spectrogram. The results show that the observed Cuvier's beaked whale clicks are a superposition of upsweep and downsweep chirps, unlike the currently accepted upsweep only structure of beaked whale clicks in bioacoustics literature. The spectral structure variations simulated by using a flat circular piston model as a beam pattern transmission model are consistent with the evolution of spectral click properties observed in experimental data. A better understanding of the properties of observed echolocation clicks of Cuvier's beaked whales will provide useful information for click annotations and, therefore, will contribute to improving accuracy of detecting, classifying, tracking, and estimating the density of Cuvier's beaked whales.

Purpose Minimally invasive surgery (MIS) has become the standard for many surgical procedures as it minimizes trauma, reduces infection rates and shortens hospitalization. However, the manipulation of objects in the surgical workspace can be difficult due to the unintuitive handling of instruments and limited range of motion. Apart from the advantages of robot-assisted systems such as augmented view or improved dexterity, both robotic and MIS techniques introduce drawbacks such as limited haptic perception and their major reliance on visual perception. Methods In order to address the above-mentioned limitations, a perception study was conducted to investigate whether the transmission of intra-abdominal acoustic signals can potentially improve the perception during MIS. To investigate whether these acoustic signals can be used as a basis for further automated analysis, a large audio data set capturing the application of electrosurgery on different types of porcine tissue was acquired. A sliding window technique was applied to compute log-mel-spectrograms, which were fed to a pre-trained convolutional neural network for feature extraction. A fully connected layer was trained on the intermediate feature representation to classify instrument–tissue interaction. Results The perception study revealed that acoustic feedback has potential to improve the perception during MIS and to serve as a basis for further automated analysis. The proposed classification pipeline yielded excellent performance for four types of instrument–tissue interaction (muscle, fascia, liver and fatty tissue) and achieved top-1 accuracies of up to 89.9%. Moreover, our model is able to distinguish electrosurgical operation modes with an overall classification accuracy of 86.40%. Conclusion Our proof-of-principle indicates great application potential for guidance systems in MIS, such as controlled tissue resection. Supported by a pilot perception study with surgeons, we believe that utilizing audio signals as an additional information channel has great potential to improve the surgical performance and to partly compensate the loss of haptic feedback.

Structure-borne acoustic emission (AE) measurement shows major advantages regarding quality assurance and process control in industrial applications. In this paper, laser beam welding of steel and aluminum was carried out under varying process parameters (welding speed, focal position) in order to provide data by means of structure-borne AE and simultaneously high-speed video recordings. The analysis is based on conventionally (e.g. filtering, autocorrelation, spectrograms) as well as machine learning methods (convolutional neural nets) and showed promising results with respect to the use of structure-borne AE for process monitoring using the example of spatter formation.

With the development of Internet of Things (IoT), predictive maintenance (PdM) for smart manufacturing receives attentions recently. For the monitoring of machines with rotary components, sound and vibration emitted from the machines have been utilized as the meaningful information. However, sound sensors are susceptible to external noise and the costs for signal conditioning should be considered. In this paper, a stethoscope is utilized as an internal sound sensor which are capable of noise reduction and affordable as IoT solutions. After the stethoscope was assembled with a microphone, a frequency range for measurement was identified as 0~255Hz using the swept-sine technique. Two stethoscope sensors were attached to an industrial robot, and the capability of noise reduction is verified by similarity of Short-Time Fourier Transform (STFT) spectrograms from sounds with and without existence of factory noise. Next, impact tests were performed to detect collision when the robot was stationary, and collisions by human hand during robot operation were detected from STFT spectrogram analysis. Finally, the Convolutional Neural Network (CNN) model from the spectrograms was established to estimate a rotating joint axis during operation, showing 91% and 90.83% accuracy for each stethoscope, respectively.

Comunicació presentada a INTERSPEECH 2021, celebrat del 30 d'agost al 3 de setembre de 2021 a Brno, Txèquia. The aim of this contribution is to automatically detect COVID19 patients by analysing the acoustic information embedded in coughs. COVID-19 affects the respiratory system, and, consequently, respiratory-related signals have the potential to contain salient information for the task at hand. We focus on analysing the spectrogram representations of cough samples with the aim to investigate whether COVID-19 alters the frequency content of these signals. Furthermore, this work also assesses the impact of gender in the automatic detection of COVID-19. To extract deep-learnt representations of the spectrograms, we compare the performance of a cough-specific, and a Resnet18 pre-trained Convolutional Neural Network (CNN). Additionally, our approach explores the use of contextual attention, so the model can learn to highlight the most relevant deep-learnt features extracted by the CNN. We conduct our experiments on the dataset released for the Cough Sound Track of the DICOVA 2021 Challenge. The best performance on the test set is obtained using the Resnet18 pre-trained CNN with contextual attention, which scored an Area Under the Curve (AUC) of 70.91 % at 80 % sensitivity. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 826506 (sustAGE) and No. 770376 (TROMPA). Further funding has been received from the FI Predoctoral Grant 2018FI-B01015 from AGAUR, Generalitat de Catalunya.

Combining sketch studies, musical analysis, and acoustic analysis, this article examines the impact of spectral models on Grisey’s creative process in the 1970s. First, it focuses on Grisey’s establishment of initial theoretical models in Dérives and Périodes, composed during his residence at the Villa Medici (1972–74), during which period he conducted his own study of acoustics. The article then looks at how, back in Paris, the composer started studying with the acoustician Émile Leipp and then worked with Michèle Castellengo, with whom in 1977 he undertook for the first time the spectral analysis of instrumental sounds. Finally the article examines in detail how some spectrograms, once transcribed, were used in Modulations (1976–77) to construct a complex spectral polyphony. If Grisey draws his inspiration from sound phenomena and the characteristics of auditory perception, he does not for all that neglect structuralism: spectral models serve both to simulate natural phenomena and to formalise his compositional processes.

Osteoarthritis is the most common type of knee arthritis that can be affected by excessive and compressive loads and can affect one or more compartments of the knee: medial, lateral, and patellofemoral. The medial compartment tends to be the most vulnerable to injuries and research suggests that a better understanding of the medial to lateral load distribution conditions could provide insights to the quantitative usage of knee compartments in activities of daily life.Prior to study in an osteoarthritic clinical population which may present with various complicating anatomical and physiological changes, we investigate knee acoustical emissions of able-bodied individuals during a varying width squat exercise which simulates loading asymmetries that would typically be seen in this clinical population. To that end, we present a novel method to quantify the directional bias of asymmetry between the medial and lateral compartment knee joint load in healthy individuals by recording knee acoustical emissions and analyzing them using a deep neural network in a subject independent model. We placed four miniature contact microphones on the medial and lateral sides of the patella on both the left and right leg. We compared the handcrafted audio features with the automated features extracted from the convolutional autoencoder which is an unsupervised model that learns the comprehensive representation of the input to determine whether these automated features can better represent the signal's characteristic in regard to the structural asymmetry of the knee joint. The input to the convolutional auto encoder (CAE) is a time-frequency representation and different types of these images such as spectrogram and scalogram are compared. We alsocompared the multi-sensor fusion approach with the performance of a single sensor to determine the robustness of using multiple sensors.Using a representation learning based approach, we developed a subject independent classification model capable of classifying the asymmetry of the medial and lateral joint load across subjects (accuracy = 83%).The result indicates that wavelet coherence which is the time-frequency correlation of two signals using a wavelet transform yields the best accuracy.These findings suggest that acoustic signals could potentially quantify the direction of medial to lateral load distribution which would broaden the implications for wearable sensing technology for monitoring cartilage health and factors responsible for cartilage breakdown and assessing appropriate rehabilitation exercises without overloading on one side.

Automated bowel sound (BS) analysis methods were already well developed by the early 2000s. Accuracy of ~90% had been achieved by several teams using various analytical approaches. Clinical research on BS had revealed their high potential in the non-invasive investigation of irritable bowel syndrome to study gastrointestinal motility and in a surgical setting. This article proposes a novel methodology for the analysis of BS using hybrid convolutional and recursive neural networks. It is one of the first methods of using deep learning to be widely explored. We have developed an experimental pipeline and evaluated our results with a new dataset collected using a device with a dedicated contact microphone. Data have been collected at night-time, which is the most interesting period from a neurogastroenterological point of view. Previous works had ignored this period and instead kept brief records only during the day. Our algorithm can detect bowel sounds with an accuracy &gt, 93%. Moreover, we have achieved a very high specificity (&gt, 97%), crucial in diagnosis. The results have been checked with a medical professional, and they successfully support clinical diagnosis. We have developed a client-server system allowing medical practitioners to upload the recordings from their patients and have them analyzed online. This system is available online. Although BS research is technologically mature, it still lacks a uniform methodology, an international forum for discussion, and an open platform for data exchange, and therefore it is not commonly used. Our server could provide a starting point for establishing a common framework in BS research.

Spectrograms provide an efficient way to analyse surface elevation signals of ship waves taken from a sensor fixed at a single point in space. Recent work based on a simplified model for the ship's disturbance suggests that matching the spectrogram heat-map patterns to a so-called dispersion curve has the potential for estimating of properties of a steadily moving ship, such as the ship's speed and closest distance to the sensor. Here we extend the theory behind the dispersion curve so that it can be applied to ships accelerating along arbitrary paths and demonstrate how acceleration affects the structure of the associated spectrograms. Examples are provided for of a simple model of a ship accelerating/decelerating in a straight line or travelling in a circle with constant angular speed. We highlight a problem with non-uniqueness of the dispersion curve when comparing ships moving along different paths. Finally, we validate the new dispersion curve against experimental results of ship models accelerating in a finite depth basin. Our work will provide a basis for more comprehensive studies that extend the simplified model to take into account the shape of the hull in question., To be published in Physical Review Fluids

We propose and numerically validate a photonic-based approach for simultaneous gap-free real-time spectral analysis (RT-SA) of multiple broadband (GHz-bandwidth) waveforms centered at different high frequency (mm-wave) bands. This exploits the concept of complex band-pass temporal sampling in a dispersion-induced time-mapped spectrogram scheme.