bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning the nuance of musical instrument acoustics
Authors: Donahue Chris
Published: November 2019

While acoustic modeling successfully captures the broad strokes of how musical instruments produce sound, it falls short of capturing the more nuanced attributes required to synthesize entirely convincing reproductions. For example, there are innumerable (effectively random) factors that affect the waveform produced by a single violinist performing the same musical gesture multiple times. Using machine learning, we can implicitly learn a distribution of these factors by modeling a collection of instrument waveforms, which could potentially lead to more convincing synthesis. In this talk, I will discuss our work on using generative adversarial networks, an unsupervised machine learning technique, to synthesize instrument waveforms. I will also speculate about how similar strategies might be paired with existing acoustic models to produce digital instruments which are indistinguishable from their real counterparts.
Convolutional neural network for chordophones recognition
Authors: Dutz Nicholas J. , Gangemi Nick , Cunningham Andrew , Kvartunas Michael , Vignola Amelia , Vignola Joseph F. , Kurdila Hannah
Published: November 2019

A student project at Catholic University related to string instruments compared the radiated sound from a guitar, a banjo and 10 ukuleles using a convolutional neural network, CNN. The objective was to determine if the CNN could be trained to distinguish between the instruments and classify them. The guitar, banjo and one of the ukuleles were factory built and the remaining 9 ukuleles where built by the team, four from kits and five from scratch. There were at least 100 single note strikes recorded for each instrument and each instrument was recorded in three different acoustic environments, anechoic, highly reverberant and moderate reverberation. This presentation will discuss the construction process used to build the ukuleles and test method used for recording. Additionally, data comparing the scratch build instruments to comparably sized factory built ukuleles will be presented to show that the CUA built instruments were appropriate for evaluation. Finally, the presentation will show the network’s predictions for instrument type and recording environment.
Electric car sound design—The paradox of almost unlimited freedom
Authors: Altinsoy M. Ercan
Published: November 2019

Electric and hybrid cars are relatively silent at low speeds. Therefore sound designers have an almost unlimited freedom regarding the electric and hybrid car indoor sound design. The extensive experiences with Active Sound Design (ASD) increases our abilities. However at least in a very near future, customer expectations are based on our long term experience with vehicle sounds with conventional engines. This point leads the designers to design new car sounds which have extensive similarities with the combustion engine car sounds. Some car manufacturers try to break this trend designing extraordinary sounds. But most of these ideas are based on previous film sound design examples and therefore the originality is limited. At the same time, the reaction of the customers to such kind of sounds is not very positive. Another important point is that there are already regulations, which define the requirements for the Acoustic Vehicle Alerting Systems (AVAS), for the exterior sound of electric and hybrid cars. In some cases, the sound of AVAS interacts with the interior sound. In this talk, the various aspects of future electric car sound design will be discussed according to our previous investigations.
Calculation of wave dispersion curves in multilayered composites using semi-analytical finite element method
Authors: Priyadarshinee Prachee
Published: November 2019

The enormous potential of Guided waves have a significant use for non-destructive evaluation(NDE) and structural health monitoring(SHM). It is common to use different solution approaches to predict dispersion curves depending on the material type, isotropic, transversely isotropic, or orthotropic, of the medium in which the wave propagates, shape of the waveguide, etc. Inability to model waveguides of arbitrary cross-section by existing matrix methods has led to development of Semi-Analytical Finite Element(SAFE) Method. In this study, rapid and accurate prediction of the propagation characteristics for guided waves in a wide range of structures has been performed by implementation of SAFE method. Damped and undamped waveguides of different shapes can be modelled. Wave propagation characteristics in both isotropic and anisotropic composite structures can also be studied. To benchmark the solution, a case study using Transfer Matrix Method (TMM) for composite plate has been evaluated. It was found that the element discretizaton plays an important role in accurate prediction of modes in SAFE method, neglecting which leads to loss of higher order modes.
Experimental analysis of the size and shape of time reversal chaotic cavities for nondestructive evaluation
Authors: Simpson Paige E. , Anderson Brian E.
Published: November 2019

Time reversal may be used as an energy-focusing technique. It is applied in many different ways including, for example, nondestructively evaluating cracks in structures, reconstructing a source event, and providing an optimal carrier signal for communication. In nondestructive evaluation applications, it is often of interest to study small samples or samples that do not lend themselves to the bonding of transducers to their surfaces. A chaotic cavity provides space for the attachment of transducers as well as a more reverberant environment, which is critical to the quality of time reversal focusing. Transducers are attached to the chaotic cavity which is attached to the sample under test. The goal of this research is to explore the dependence of the quality of the time reversal focusing on the size and shape of the chaotic cavity used. An optimal chaotic cavity will produce the largest focusing amplitude, best spatial resolution, and most linear focusing of the time reversed signal. The presentation will provide experimental results for time reversal focusing experiments conducted on various sized and shaped aluminum blocks.
Structural vibration analysis of Fresco cohesion
Authors: Cunningham Andrew , Vignola Amelia , Turo Diego , Corvaro Francesco , Ryan Teresa J. , Vignola Joseph F. , Marchetti Barbara
Published: November 2019

One approach for non-contact interrogation of mixed-media surfaces, such as fresco, is mapping surface motion induced by acoustic excitation. A scanning laser Doppler vibrometer provides such high spatial resolution surface information which can indicate material inclusions, delaminations, and other subsurface features. This work uses this interrogation method on frescos in the U.S. Capitol Building. These frescos were installed in the mid nineteenth century under the supervision of Italian artist Constantino Brumidi. Brumidi and his team used traditional fresco techniques for their work throughout the U.S. Capitol Building. Specifically, this work focuses on the Senate Reception Room. Such maps of these frescos were completed in 2004. In the intervening years, external events may have changed the fresco integrity. Such events include restoration work performed by the Office of the Architect of the Capitol and the Mw 5.8 earthquake in Mineral, Virginia in 2011. These repeat measurements allow for an assessment of changes due to these events. While these measurements can only locate the region of anomaly, an inverse analysis is required to classify the nature of the anomaly. Therefore, in addition to measurements, numerical finite element simulations are used to classify the type, size, and location of defects.
Neural commitment alters cue weighting of formant structure in speech perception: A cross-language MEG study
Authors: Zhang Yang , Kuhl Patricia , Imada Toshiaki , Tanaka Keita
Published: November 2019

This magnetoencephaalography (MEG) study investigated the effects of language experience in cue weighting of formant structure supporting native language neural commitment. The speech stimuli were a grid of /ra-la/ synthetic continuua with systematic variations of the second (F2) and third (F3) formants. The subjects were a group of 10 monolingual adults of American English speakers and a group of 10 adult Japanese speakers. The experimental protocol adopted bi-directional oddball paradigm to examine between-group differences in discriminatory sensitivity at the pre-attentive level to stimulus conditions that featured F2 difference alone, F3 difference alone and F2 + F3 differences. Unlike previous reports, the MEG data showed equivalent amplitudes of mismatch responses for discriminating the /ra-la/ contrast in the F2 + F3 condition between the American and Japanese subjects. In the F2 difference alone condition, Japanese subjects showed enhanced discriminatory sensitivity than Amercian subjects. In the F3 difference alone condition, Japanese subjects showed reduced sensitivity than American subjects. Together, the MEG results provide strong support for an altered pattern of cue weighting that explains the native language perceptual interference in speech categorization.
Analysis and modeling for modification of speaking to karaoke-style singing
Authors: Khorram Soheil , Hansen John H.
Published: November 2019

This study considers an analysis of differences in voice modification within the domains of speaking and singing the same text content, resulting in the first attempt in converting speaking to Karaoke-Style singing. To develop this system, we collected a new dataset, UT-Sing dataset, containing more than 23 h of speech from 81 participants across four different languages: English, Farsi, Hindi, and Mandarin. We asked each participant to produce the same text content by first reading and then singing 5 popular songs while listening to the accompanying instrumental music through open-air headphones. This effectively creates a parallel dataset that is suitable for voice modification systems. We first use this dataset to compare different prosodic and spectral characteristics of Karaoke-style singing versus speaking the same text content. We then leverage the knowledge obtained from this comparison to develop a speaking to Karaoke-Style singing modification system. In the training phase of the developed system, we extract acoustic features using the WORLD vocoder; we align the acoustic features through the DTW algorithm. Finally, we train a residual network to model the relationship between the acoustic features. We employ subjective assessments to evaluate the performance of the developed system.
Accuracy of the language environment analysis (LENA) speech processing system for detecting communicative vocalizations of young children
Authors: Woodard Jaie C. , Losievski Nikaela , Arjmandi Meisam K. , Lehet Matthew , Wang Yuanyuan , Houston Derek , Dilley Laura
Published: November 2019

Automated audio processing systems, such as the Language Environment Analysis (LENA) system, are useful tools for understanding developmental language behaviors for clinical and basic research purposes. However, it is still unclear how accurate they may be in comparison to the traditional gold standard of evaluation by trained human listeners. In our study, human coders identified starts and ends of communicative vocalizations of children and adults from sampled audio in day-long LENA recordings of 23 families with a child with variable hearing status; accuracy of LENA was then determined for each recording by comparing LENA and human-derived labels for 100-ms frames of sampled audio. Preliminary analysis suggests that LENA accurately identified communicative vocalizations of the target child wearing the device as being produced by that target child 65% of the time (35% error); accuracy ranged from 49%—79% across recordings. When any child vocalization was correctly identified, LENA accurately distinguished whether this belonged to the target child or another child 75% of the time (25% error); accuracy, however, ranged from 7%—96%. These accuracy levels suggest caution is needed in applying popular speech processing systems like LENA to clinical and scientific questions in absence of additional validation measures.
Automatic perceptual judgment using neural networks
Authors: Park Seongjin , Culnan John
Published: November 2019

The aim of the present study is to investigate the performance of automatic perceptual judgment models built with neural networks. In previous studies, Franco et al. (1997) used HMM-derived scores based on posterior probabilities of phone segments, demonstrating a high correlation with human raters. Deville et al. (1999) also used an HMM/ANN recognition approach, and showed how the results of automatic speech recognition can be used for perceptual judgments. However, since most previous studies made use of automatic speech recognition in their analysis, the present study provides a different approach: using features and raw data. Native speakers of English will listen to English sentences produced by native and non-native speakers of English, transcribe what they heard, and respond to one of three perceptual judgements: foreign-accentedness, fluency, and comprehensibility. The data will be fed into prediction models in three different ways; one with annotated features (pauses, durations, etc), another with Mel Frequency Cepstral Coefficients (MFCC), and the other with Mel-spectrograms. The performance of the models will be measured by analyzing the correlation between the judgments by models and by human raters. The preliminary results of this study will be used to build more accurate automatic proficiency judgment models.
An end-to-end approach for true detection of low frequency marine mammal vocalizations
Authors: Thomas Mark , Martin Bruce , Kowarski Katie , Gaudet Briand , Matwin Stan
Published: November 2019

Research into automated systems for detecting marine mammal vocalizations within acoustic recordings is expanding internationally due to the necessity to analyze large collections of data collected for passive acoustic monitoring. Recent work towards the development of such systems using Convolutional Neural Networks (CNNs) shows great promise and these systems are capable of generalizing to additional species without having to re-train the entire network [1]. However, to the best of our knowledge, the current deep learning implementations do not perform what we refer to as true detection. Instead these systems are simply capable of determining the presence or absence of a vocalization within a spectrogram. In this work we present a CNN trained on spectrograms containing labelled bounding boxes around low-frequency vocalizations produced by several species of marine mammals. In this way, the CNN can precisely detect vocalizations in terms of both time and frequency, while maintaining the advantage of being generalizable to additional species. [1] M. Thomas, B. Martin, K. Kowarski, B. Gaudet, and S. Stan, "Marine mammal species classification using convolutional neural networks and a novel acoustic representation," in ECML PKDD 2019 (Springer, Cham, 2019).
Investigation of obstacle avoidance algorithm in paired-driving autonomous mobile robots revealed by mimicking ultrasonic sensing in bats
Authors: Kubota Tomoya , Nakade Shoya , Yamada Yasufumi , Asada Takaaki , Sasaki Shinichi , Hiryu Shizuko
Published: November 2019

To evaluate the biosonar behavior of bats in paired flight from an engineering point of view, obstacle avoidance driving experiments were conducted using two autonomous mobile robots embedded with one ultrasound transmitter and two receivers. It has been reported that when two bats aim at the same prey simultaneously, the following bat temporarily stops the pulse emission and tracks the preceding bat. It is supposed that this is to avoid interference with signals by other individuals, i.e., utilizing pulses emitted by other individuals and the echoes for their own sensing. Then we proposed the hybrid sensing which switches active and passive sensing according to the situation, and the completion rate of obstacle route in the paired-driving robots was examined. As the result, it was proven that hybrid sensing robot reduced the emission frequency of the pulse, but the completion rate of the obstacle route was high in comparison with case of the paired-driving robots with active sensing. In addition, in order to improve localization performance and to emit FM type ultrasonic pulse of bats, thermoacoustic transducer (thermophone) with broadband frequency characteristics are introduced as a ultrasound transmitter. [Work supported by JSPS-KAKENHI;JP18H03786, JP16H06542 and JST-PESTO.]
Deep neural network based direction of arrival estimation for hearing aid applications using smartphone
Authors: Küçük Abdullah , Panahi Issa M.
Published: November 2019

Deep neural network (DNN) techniques are gaining popularity due to performance boost in many applications. In this work we propose a DNN-based method for finding the direction of arrival (DOA) of speech source for hearing aid applications using smartphones. We consider the DOA estimation as a classification problem and use the magnitude and phase of speech signal as a feature set for DNN training stage and obtaining appropriate model. The model is trained and derived using real noisy speech data recorded on smartphone in different environments under low SNRs. The DNN-based DOA method, with the pre-trained model, is implemented and run on Android smartphone in real time and evaluated objectively and subjectively. The test results are presented showing the performance of proposed method versus other methods.
Self-fit generation of the wide range compression parameters in hearing aids
Authors: Bose Apurba , Gan Ziqi , Garudadri Harinath
Published: November 2019

OpenMHA is an open source software executing the real time signal processing algorithms with reduced latency between the input and the output signals. In this abstract, we elaborate on a hearing aid fitting algorithm, which tunes the Wide Dynamic Range Compression (WDRC) algorithm parameters through a A-B comparison test. The following approach is adopted-The left and the right audiogram values are clustered using the Gaussian Mixture model. A value is assigned to more than one cluster with a probability. The cluster labels are assigned according to the model yielding the maximum log likelihood-The best fit for the audiogram values of user is used to calculate the WDRC parameters. Recording the audiogram values of individual user and tuning the parameters by the audiologists can be a cumbersome task. Web-app is implemented to present A-B comparison to the users to determine the best fit audiogram values. The Open Speech Platform (OSP) comprises of Embedded Web Server (EWS) presenting the web-app. It guides the user to determine the best fit through a AVL tree based binary search algorithm. The device aims to determine the best fit in reduced number of steps, post which the WDRC parameters are determined through the NAL-NL2 libraries. This search algorithm for each user, and listener’s feedback on the success of the fit using the ecological monetary assessment (EMA) app, with the environmental sound data logging can be leveraged further to create auto fit algorithms in dynamic environments using MCTS in reinforcement learning.
A deep learning approach to source localization and seabed classification using pressure time-series from a vertical array
Authors: Acree Mason C. , Van Komen David F. , Neilsen Tracianne B. , Knobles David P.
Published: November 2019

Source localization in a shallow ocean environment has historically been done using optimization techniques such as matched-field processing. However, such optimizations depend on the parameterization of the ocean environment. Due to the complexity of this physical system, some researchers are currently applying machine and deep learning techniques to source localization problems. We propose a convolution neural network (CNN) to better predict the source localization and seabed classification simultaneously using pressure time series waveforms from a vertical line array. Building on research using a CNN to classify the source locale and seabed type using waveforms from only one hydrophone, the method has been extended to a 16-element vertical line array. The additional hydrophones add more physical information from the system as well as simply more features for a CNN to learn source range, depth, and seabed type. The synthetic data were generated using a range independent normal-mode model for multiple ocean environments. Modifications to the CNN are made to exploit the multi-channel waveforms. Ocean acoustic applications require this accurate classification of source locale and seabed environment. In future work we will extend our technique and CNN model to work with real world data.
Investigation on one-step integrated positioning method for seafloor control network localization
Authors: Zhao Shuang , Wang Zhenjie
Published: November 2019

Global Navigation Satellite System-Acoustic (GNSS-A) combined positioning is the main technique for seafloor geodetic control points localization, typically including datum transfer and subsequent positioning adjustment. This conventional step-wise method is time-consuming and accuracy-limited. In this paper, a novel one-step integrated positioning method for seafloor control network localization is proposed, which estimates positions of surveying vessels and seafloor points simultaneously. The effectiveness of new method is verified by computer simulation and comparative analysis. First, the conventional underwater acoustic positioning stepwise adjustment method is introduced and the positioning errors are analyzed. Then, to better constrain positions, the baseline lengths and depth-differences between pair of seafloor transponders are taken into consideration. The novel integrated positioning model based on overall adjustment is established. Furthermore, to deal with rank deficiency problem in overall adjustment, selecting weight fitting (SWF) method is applied in consideration of reliable priori information of surveying vessel coordinates. Finally, the regulating parameter determination for SWF method is discussed. Tests demonstrate that depth-differences constraint performs better than baseline length constraint in light of vertical geometry weakness. The novel method is notably superior to the conventional method in terms of positioning accuracy according to rms (Root Mean Square) statistics.
Acoustic camera modeling and compensation technique based on SONAR equation
Authors: Bae Ho Seuk , Kim Woo-Shik
Published: November 2019

Acoustic camera is a type of ultrasonic equipment used to detect or classify nearby objects located in water. The distortion of SONAR images acquired from conventional acoustic camera equipment inevitably occurs depending on the platform speed and operating frequency. This has not been huge concern because the platform speeds were low. However, more recently, a high accuracy of acoustic camera images has become essential in accordance with the development of unmanned autonomous systems. Additionally, a high speed of operating platform is also required. Therefore, there is a need for the study of image correction to reduce the distortion of SONAR images acquired from the equipment. In this study, we simulate virtual acoustic camera SONAR images by performing numerical modeling based on the SONAR equation to improve the accuracy of acoustic camera images. To verify the validity of the simulated images, we use the literature data and water-tank experiment data. Finally, we conduct the image compensation both in the direction of motion relative to the platform speed, and in the transverse direction relative to the platform speed and operating frequency.
A low-cost, autonomous surface vehicle-based system for unexploded ordnance bistatic acoustic localization and classification
Authors: Fischell Erin , Manganini Kevin , Plotnick Daniel
Published: November 2019

A growing application for unmanned vehicle technology is the localization, classification and mitigation of underwater hazards such as munitions in shallow harbor environments. Because visual inspection of targets can be difficult or impossible in murky harbors and requires precise target localization, acoustic sensors such as sidescan sonar and synthetic aperture sonar (SAS) are used more extensively for munition detection missions. While these techniques can provide rich images of targets and the environment, postprocessing is often required and the sensors themselves are too expensive to be practical in multi-vehicle operations. A new system for low-cost, autonomous-surface-vehicle-based unexploded ordnance detection and localization is under development to provide a practical alternative to provide greater coverage via distributed, multi-vehicle missions. A mobile receiver array, fixed source geometry makes it possible to cover large areas while detecting man-made, aspect-dependent targets based on directional scattering from seabed targets. The system built on a Woods Hole Oceanographic Institution JetYak autonomous surface vehicle and results from experiments in summer 2019 in Ashumet Pond in Falmouth, Massachusetts are presented. [Sponsored by SERDP.]
Model-aided acoustic environment estimation via data fusion for autonomous underwater vehicles
Authors: Viquez Oscar A. , Bhatt EeShan C. , Novitzky Michael , Schmidt Henrik
Published: November 2019

In autonomous underwater vehicle (AUV) operations, high-fidelity acoustic and environmental models are necessary for facilitating autonomous behaviors. Our current method uses a virtual ocean simulator driven by environmental empirical orthogonal functions, which describe a pre-determined set of sound speed perturbations, and their corresponding recombination coefficients, weights assigned to each function to recreate a specific sound speed profile that can be updated via acoustic communication. However, this approach is limited by its reliance on pre-processed theoretical models of ocean dynamics and sparse CTD sampling, which is especially concerning in rapidly varying environments. As the mission is underway, it is advantageous for the vehicle itself to develop a better estimate of said recombination coefficients by accounting for on-board acoustic and environmental sensor data. We present an initial framework for bounding the estimate of the sound speed profile recombination weights against acoustic propagation models, under the constraints of a band-limited collaborative acoustic network and the risk-averse nature of AUV operations. [Work supported by the Office of Naval Research.]
Acoustic sculpting revisited
Authors: Mahalingam Ganapathy
Published: November 2019

The concept of acoustic sculpting was introduced in the early 1990s through a series of papers by the author. Acoustic sculpting is the process of generating the forms of spaces based on desired acoustical performance criteria for those spaces. These performance criteria are in the form of well known room acoustic parameters, such as the time-delay gap and clarity. This paper will revisit the methods of acoustic sculpting as described in the earlier papers and make efforts to strengthen the methods and expand the scope of acoustic sculpting. By critically analyzing room acoustical parameters and the information embedded in them, new ways to map that information into a process to generate spatial forms will be articulated. The methodology will be a process of mapping two different kinds of information that are directly related, because the parameters of one are responsible for the generation of the parameters of the other. In traditional acoustical simulation, the spatial parameters of a space are known and the room acoustical parameters are derived through simulations. In the process of acoustic sculpting, this process is reversed, and the acoustical parameters are used to derive the spatial parameters. The purpose of developing the methods of acoustic sculpting is to use the mapping to create software systems for the generation of spatial forms for acoustical performance, such as the spatial forms of concert halls, auditoriums, theaters, and classrooms.
Urban spectralisms: Relational design approaches for orchestrating and sharing space
Authors: Schütz Nadine M.
Published: November 2019

If the study and design of architectural soundscapes stick to Euclidian notions of space, they risk overhearing the heterogeneous nature of auditory spatial experience beyond location and distance. A rich range of new musical aesthetics has led to the summarizing notion of spectralisms in the 20th century, describing compositional approaches built on mutual acoustic and perceptual considerations. But the design potential inhering this approach goes far beyond the realm of music. Spectral-temporal structures are key to grasping the spatial richness of our sonic environment. In urban planning, however, these dimensions are only considered as impacting the sensations evaluated through the psychoacoustic model of auditory pleasantness referring to single sounds and thus neglected as a domain of spatial formation. This paper argues that a mindful application of perceptual mechanisms related to the 'inner' acoustic structure of sound opens up new approaches for orchestrating and sharing urban space more efficiently. Through concrete project examples—such as the Garden of Reflections for the La Défense plaza or the Acoustic Niches for the forecourt of the new courthouse in Paris—it introduces spectral thinking as a conceptual tool for understanding and designing different forms of acoustic co-presence, comfort, and relation in our cities.
Fine-scale classification of odontocete echolocation clicks using deep learning
Authors: Rafter Macey , Frasier Kaitlin E. , Trickey Jennifer S. , Wiggins Sean M. , Hildebrand John
Published: November 2019

All toothed whale species produce echolocation clicks, which can be used for passive acoustic monitoring. However in biodiverse habitats, such as off the coast of southern California, it is common for acoustic encounters with multiple species to overlap in time. This creates a challenge for acoustic classification in the context of long term monitoring. Machine learning methods may facilitate classification at the level of individual detections. A generic echolocation click detector was applied to passive acoustic recordings from the 2013 DCL dataset, which has manual labels of five odontocete species encounters available as a ground truth. We evaluated two different methods for training deep neural networks to classify the detections to species, using either individual click waveforms or clustered sets of similar echolocation clicks. Results show that deep neural networks have the potential to accurately classify clicks to species at fine scales, allowing for improved handling of complex acoustic environments and multi-species encounters. We suggest that click-level classification can facilitate more advanced quantitative analysis of passive acoustic recordings, and that it can be done efficiently using deep learning.
Automated classification of dolphin whistles based on the convolutional neural network
Authors: Qiao Gang , Li Lei , Liu Songzuo , Qing Xin
Published: November 2019

A number of classification systems for dolphin whistles are applied to study the relation between dolphin whistles and their behaviors. Traditional approaches require prior knowledge, numerous pre-processing and manually extracting the features by conventional signal processing methods. In this study, deep convolutional neural networks are used to classify whistles and automatically learn the sound characteristics from training data with less pre-processing. The classification system is trained by using a database of 9 sorts of measured dolphin signals with nearly 4000 samples, and whistle contours in testing dataset are divided into six types, including constant frequency, upsweep, downsweep, concave, convex, and multiple. Finally, more than 90% for the classification accuracy rate is reached, and results show insensitivity to background noise. Therefore, the algorithm can be employed to study the potential relation between dolphin whistle signals and behaviors, and then facilitate future studies on dolphin habits.
Deep whistle contour: Recall-guided learning from synthesis
Authors: Li Pu , Liu Xiaobai , Fleishman Erica , Palmer Kaitlin , Shiu Yu , Klinck Holger , Helble Tyler A. , Gillespie Douglas M. , Nosal Eva-Marie , Cholewiak Danielle , Roch Marie
Published: November 2019

This paper presents a learning based method for detecting whistles of toothed whales from underwater hydrophone recordings. Our method represents audio signals as time-frequency spectrogram and employs the Fully Convolution Network (FCN) to estimate for each spectrogram a map of contour confidences that are used for extracting discrete whistle contours. To avoid the expensive efforts of annotating whistle contours, we develop a data synthesis approach to generate spectrogram-contour pairs using spectrogram of background environment and a small set of whistle contours. Our study suggests that the deep contour model can be effectively learned from these synthesized samples. However, it is costly and unnecessary to synthesize equal amount of samples for each spectrogram or contour. Instead, we present an alternative learning algorithm that synthesize samples only for those spectrogram or contours that are not well modeled by the current network, measured by recall rates of contour points for each spectrogram-contour sample. This recall-guided learning algorithm can adaptively synthesize difficult samples to boost learning effectiveness. We applied the proposed method to the public DCLDE2011 dataset to extract whistle contours. Results show that our method can improve state-of-the-art method up to 21.9% in terms of F-score for multiple odontocete species.
Improvements in signal processing for the estimation of environmental parameters with machine learning using a compact tetrahedral array and sources of opportunity
Authors: Moore Jesse T. , Miller James H , Potty Gopu R. , Lin Ying-Tsong , Bonnel Julien , Newhall Arthur
Published: November 2019

In previous work, we showed that we could localize sound sources using a compact tetrahedral hydrophone array in a continental shelf environment south of Block Island, Rhode Island. The tetrahedral array of phones, 0.5 m on a side, was deployed to monitor the construction and operation of the first offshore wind farm in the United States. Directions of arrival (DOAs) for a number of ships were computed using a time difference of arrival technique. Given the DOAs, ranges are estimated using supervised machine learning techniques. We extended that work to estimate a number of environmental parameters including water depth and sediment composition. Here, we report on results using new spectrogram processing techniques based on high resolution PE modeling. These results include inversions for sediment parameters with estimates of error. With this new higher resolution spectrogram processing, we also report on the impact of the sediment parameters on range estimation. We generalize the technique to generic continental shelf environments. [Work supported by the Office of Naval Research.]
Trans-dimensional inversion of modal dispersion data collected by an underwater glider
Authors: Jiang Yong-Min , Dosso Stan E. , Bonnel Julien , Wilson Preston S.
Published: November 2019

Acoustic payload equipped underwater gliders have been proven to have great potential for maritime intelligence, surveillance, and reconnaissance missions, as well as oceanic environment characterization. This paper demonstrates capabilities for seabed characterization using broadband signals received on a hydrophone-equipped Teledyne Webb Research Slocum glider during the 2017 Seabed Characterization Experiment (SBCEX) conducted on the New England Mud Patch. In the experiment, a source ship maintained a fixed position while two combustive sound-source signals were emitted at a separation of about two minutes in time. The glider was programmed to follow a sawtooth-like track through the water approximately 8 km from the source. Both source and glider were in an area where water depth is around 72 m. The two transmissions were received by the glider at depths separated by about 15 m. Discrepancies in the modal dispersion structure of the received signals were observed, as expected for different reception depths. Trans-dimensional geoacoustic inversion is applied to the modal-dispersion data to study the consistency of the inversion results for signals received at different depths. The benefit of combining signal receptions at different depths in the inversion to reduce the uncertainties of the geoacoustic estimates is also addressed. [Work supported by ONR.]
Multi-modal assessment of histotripsy liquefaction
Authors: Bader Kenneth B. , Anthony Gregory , Bollen Viktor , Hendley Samuel A. , Sammet Steffen
Published: November 2019

Histotripsy is an emerging therapeutic ultrasound modality for mechanical tissue liquefaction via bubble cloud activity. Image guidance schemes for histotripsy necessitate both quantification of the bubble cloud activity and accurate delineation of the treatment zone. In this study, magnetic resonance (MR) and passive cavitation imaging (PCI) were combined to assess histotripsy treatment in vitro and ex vivo. Bubble cloud emissions were monitored with PCI. Changes in the medium structure were assessed with T1-, T2-, and diffusion-weighted MR images. Liquefaction zones generated in agarose phantoms and porcine livers were correlated with passive cavitation and MR images through receiver operating characteristic (ROC) analysis. Strong bubble activity was observed for all samples. Histotripsy-induced changes in sample structure were evident on T2-weighted images for all samples, and T1- and diffusion-weighted imaging for liver samples. The locations of the strongest MR pixel values did not necessarily coincide with the locations of the most intense bubble activity. The area under the ROC curve for predicting liquefaction was significantly greater than 0.5 for all imaging modalities, and PCI provided the best prediction of histotripsy damage. These results indicate PCI and MR imaging provide complementary sets of information, demonstrating the utility of multimodal imaging for monitoring of histotripsy.
Use sonography to assess swallowing in post-thyroidectomy patients
Authors: She Yunying , Chen Hsinhua
Published: November 2019

Every surgery which involved the patient’s neck may have the chance to affect muscle kinetics related to functions of swallowing and voicing. Ultrasound can be a non-invasive diagnostic tool and provides the real-time image to visualize muscle kinetics. A prospective case control study. The aim of our study was to record the swallowing movements of patients after uncomplicated thyroidectomy postoperative 4–6 weeks and postoperatively 6 months using ultrasound . The patient will drink 10 ml water continuously with a repetitive saliva swallowing test. Analyzing the sonographic images to record the movement of cricoids cartilage while swallowing, the elevated distance of the laryngeal box and the thickness of the belly of straps muscles to differentiated the inflammation status of muscles. Comparing these data with the normal cases can help us to define the diagnostic cut-off value for dysphagia and quantization the neck strangling objectively. The result revealed that the swelling of the strap muscles will affect the efficacy of laryngeal elevation and the motion of cricoid cartilage during the swallowing. This kind of examination for swallowing evaluation is convenient, quick, non-radiation, noninvasive, can help us to realize and focus on the real swallowing problem of the patient and improve the efficacy of the logopedic therapy.
Automatic detection of involuntary spasticity of major muscles with acoustic and electromyography
Authors: Lawal Iyabo , Kirn Herbie , Di Trani Nicola , Pons-Faudoa Fernanda P. , Paez-Mayorga Jesus
Published: November 2019

Spasticity is a severely debilitating condition where involuntary muscle contraction impedes normal movement, speech and gait. It usually results from motor root damage in patients with history of multiple sclerosis, traumatic brain injury, or stroke. This damage causes misfiring of neurons leading to erratic muscle contraction. Current treatments inhibit muscle activation, however, treatment is given upon onset of the spastic episode, delaying symptom alleviation. Here we developed a sensing device combining surface acoustic myography (sAMG) and surface electromyography (sEMG) for the automatic detection of spasticity. sAMG use acoustic-based sensors to detect pressure waves produced by muscle contraction whereas sEMG detect electrical signals from neuromuscular activity. Merging sEMG and sAMG sensors provided a robust set of spectral data to describe muscle contraction patterns in terms of electromechanical activities. Said patterns were used for calibration using machine-learning algorithms. With established normal patterns, the device can distinguish between voluntary and involuntary muscle movement. In sum, the symbiotic employment of both types of sensors shows potential in early detection of a spastic episode. Being able to detect the onset or to predict a spastic event can provide insight into the underlying physiological conditions and, ultimately, help develop technologies to mitigate its occurrence.
A nonlinear lumped parameter model for designing a capacitive MEMS microphone
Authors: Kim Chayeong , Ahn Hongmin , Lee Yoon Jong , Lee Han Choon , Jung Minhyun , Moon Wonkyu
Published: November 2019

The MEMS microphone, which is widely used for mobile devices, includes electro-mechanical energy conversion as well as acousto-mechanical conversion through the structures such as the back plate with many holes and an extremely flexible membrane with nonlinear characteristics. Hence, it is a time-consuming job to build an appropriate finite element model to predict the dynamic behaviors of the microphone. The equivalent circuit models are frequently used to check the linear behaviors of the MEMS microphone. However, it is sometimes inadequate to predict the nonlinear behaviors due to the nonlinear deformation of the sensing membrane, which usually determine the acoustic overload point. In this study, a nonlinear four-degree-of-freedom model is developed to design the MEMS microphone. Instead of the conventional equivalent circuit model, a model based on mechanical analysis is constructed and the state equations for the model is derived in the form of a set of ordinary differential equations. The responses of the model can be easily predicted in the time and frequency domain both by solving the equations numerically. With the time domain analysis used, the sensitivity can be easily shown as a function of frequency even including the nonlinear dynamic behaviors. [Work supported by DB Kim Jun-ki Cultural Foundation.]
Microelectromechanical systems directional acoustic sensor for underwater applications
Authors: Roberts Jason , Espinoza Alberto , Alves Fabio , Rabelo Renato , Karunasiri Gamani
Published: November 2019

In this work, a MEMS-based directional acoustic sensor operating underwater is explored. The sensor operates in a narrow frequency band centered at the mechanical resonance. The studied sensor consists of two wings coupled by a bridge which is pivoted to a substrate. Interdigitated comb finger capacitors attached to the wings allow for electronic readout of the mechanical oscillations, which are proportional to the sound direction of incidence. For underwater testing, the sensor was immersed in silicone oil, contained by a urethane housing with near unity acoustic transmission. The characteristics of the MEMS sensor both in air and silicone oil were analyzed using finite element modeling. Performance of the sensor was characterized both in air and underwater. Measured underwater frequency response showed that the resonance frequency of the sensor was shifted to a lower value compared to that of in air. This is primarily due to mass loading from the silicone oil used for immersing the sensor. Peak sensitivity of the sensor was found to be about 6 mV/Pa or –165 dB re 1 V/mPa. The sensor showed good directional response with a dipole pattern. Results show the potential of MEMS sensors for underwater applications to detect the bearing of sound sources.
Phase and amplitude correction of microphones for infrasound vector intensity using the noise field from a rocket motor
Authors: Irarrazabal Francisco J. , Cook Mylan R. , Gee Kent L. , Sommerfeldt Scott D. , Rasmussen Per
Published: November 2019

This paper describes the phase and amplitude correction of 12.7 mm diameter, Type-1 microphones in the infrasound region using the measured noise field of a large rocket motor. The data stem from acoustic intensity measurements using two-dimensional, three and four-microphone probes, which require that the acoustic phase and amplitude differences be much greater than the interchannel mismatch. Although processing for correcting the amplitude/phase is well-known, obtaining the necessary transfer functions in the infrasound regime is challenging because (1) signal-to-noise ratios are often poor, (2) long measurement times are required for averaging, and (3) microphone responses vary significantly across these low frequencies. Thus, a convenient noise source for performing a calibration is a static firing of a solid-fuel rocket booster. Far-field measurements of a large motor test greater than 1 min in length provide a known propagation direction and expected equivalence of significant, low-frequency amplitudes, which allows for correction of the microphone transfer functions at low frequencies. The performance of the calibration method, including limitations, for different microphones and probe configurations, is discussed. [Work supported by NSF.]
Ambiguity resolution using dual frequency transducer for acoustic Doppler current profiler applications
Authors: Sarangapani Sairajan
Published: November 2019

Broad Band Acoustic Doppler Current Profilers (ADCPs) have been used for over past 20 years to measure the relative velocity measurements in rivers and ocean environments but are inherently subjected to velocity ambiguities due to the phase change in the binary phase shift code. A new dual frequency transducer capable of transmitting and receiving at two different frequencies and having the same beam width is described here and is shown to resolve the inherent velocity ambiguity, thereby having velocity measurements with high accuracy and profile longer ranges from a single instrument. The electro-acoustic transducer made out of a piezoceramic in the shape of disc, vibrating in the extensional mode of operation is used in this approach to generate the directional acoustic radiation in the direction normal to the face of the transducer. The dual frequency transducer was integrated with the new generation ADCP-3 electronics, to transmit and receive sound underwater. An experimental investigation was conducted off San Diego coast using the dual frequency transducer operating at 2 different frequencies namely the low frequency at 300 kHz and high frequency at 1200 kHz in a single instrument and results are presented as a function of range and velocity measurements.