Acoustic Signals Research Articles

The relative contribution of acoustic signals versus movement cues in group coordination and collective decision-making.

To benefit from group living, individuals need to maintain cohesion and coordinate their activities. Effective communication thus becomes critical, facilitating rapid coordination of behaviours and reducing consensus costs when group members have differing needs and information. In many bird and mammal species, collective decisions rely on acoustic signals in some contexts but on movement cues in others. Yet, to date, there is no clear conceptual framework that predicts when decisions should evolve to be based on acoustic signals versus movement cues. Here, we first review how acoustic signals and movement cues are used for coordinating activities. We then outline how information masking, discrimination ability (Weber's Law) and encoding limitations, as well as trade-offs between these, can identify which types of collective behaviours likely rely on acoustic signals or movement cues. Specifically, our framework proposes that behaviours involving the timing of events or expression of specific actions should rely more on acoustic signals, whereas decisions involving complex choices with multiple options (e.g. direction and destination) should generally use movement cues because sounds are more vulnerable to information masking and Weber's Law effects. We then discuss potential future avenues of enquiry, including multimodal communication and collective decision-making by mixed-species animal groups. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamic'.

Exploring the role of vocalizations in regulating group dynamics.

Because of the diverging needs of individuals, group life can lead to disputes and competition, but it also has many advantages, such as reduced predation risk, information sharing and increased hunting success. Social animals have to maintain group cohesion and need to synchronize activities, such as foraging, resting, social interactions and movements, in order to thrive in groups. Acoustic signals are highly relevant for social dynamics, some because they are long-ranging and others because they are short-ranging, which may serve important within-group functions. However, although there has been an increase in studies concentrating on acoustic communication within groups in the past decade, many aspects of how vocalizations relate to group dynamics are still poorly understood. The aim of this review is to present an overview of our current knowledge on the role of vocalizations in regulating social group dynamics, identify knowledge gaps and recommend potential future research directions. We review the role that vocalizations play in (i) collective movement, (ii) separation risk and cohesion maintenance, (iii) fission-fusion dynamics, and (iv) social networks. We recommend that future studies aim to increase the diversity of studied species and strengthen the integration of state-of-the-art tools to study social dynamics and acoustic signals. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

The ecology of zebra finch song and its implications for vocal communication in multi-level societies.

Acoustic signalling is crucial in affecting movements and in social interactions. In species with dynamic social structures, such as multi-level societies, acoustic signals can provide a key mechanism allowing individuals to identify and find or avoid each other and to exchange information. Yet, if the spacing between individuals regularly exceeds the maximum signalling range, the relation between movements and signals becomes more complex. As the best-studied songbird in captivity, the zebra finch (Taeniopygia castanotis) is a species with individually distinct songs that are audible over just a few metres and a widely ranging dynamic multi-level social organization in the wild, raising questions on the actual role of its song in social cohesion and coordination. Here, we provide an overview of birdsong in social organizations (networks) and use the ecology of the zebra finch and male song to discuss how singing can facilitate social cohesion and coordination in species where the signal range is very short. We raise the question of the extent to which zebra finches are a representative species to understand the function of song in communication, and we broaden current views on the function of birdsong and its individual signature. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Using machine learning techniques in inverse problems of acoustical oceanography

AbstractThe goal of the work presented here is to study a novel approach for inverting acoustic signals recorded in the marine environment for the estimation of environmental parameters of the water column and/or the seabed. The proposed approach is based on signal feature extraction using a discrete wavelet packet transform, applied to the measured signal, and hidden Markov models that exploit the sequential patterns of the signals. The signal feature is thereafter used in the framework of a mixture density network, which, after training with sets of simulated signals calculated within a predefined search space, provides conditional posterior distributions of the recoverable parameters. The technique is tested with two test cases corresponding to different types of inverse problems. The first case corresponds to a simple problem of geoacoustic inversion, while the second is referred to a, rather unusual, still interesting problem of recovering the shape of a seamount using long‐range acoustic data. Both test cases are based on simulated experiments. The inversion results obtained using the proposed scheme are compared with inversion results using statistical features of the acoustic signal, which is another inversion approach well documented in the literature and is also based on the wavelet packet transform of the measured signal.

Not your private tête-à-tête: leveraging the power of higher-order networks to study animal communication.

Animal communication is frequently studied with conventional network representations that link pairs of individuals who interact, for example, through vocalization. However, acoustic signals often have multiple simultaneous receivers, or receivers integrate information from multiple signallers, meaning these interactions are not dyadic. Additionally, non-dyadic social structures often shape an individual's behavioural response to vocal communication. Recently, major advances have been made in the study of these non-dyadic, higher-order networks (e.g. hypergraphs and simplicial complexes). Here, we show how these approaches can provide new insights into vocal communication through three case studies that illustrate how higher-order network models can: (i) alter predictions made about the outcome of vocally coordinated group departures; (ii) generate different patterns of song synchronization from models that only include dyadic interactions; and (iii) inform models of cultural evolution of vocal communication. Together, our examples highlight the potential power of higher-order networks to study animal vocal communication. We then build on our case studies to identify key challenges in applying higher-order network approaches in this context and outline important research questions that these techniques could help answer. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Stratified water columns: homogenization and interface evolution

Stratified water columns are often found in lakes and oceans. Stratifications result from differences in density due to salt concentration, temperature, solid content and oxygenation. The stability of stratifications affects bioactivity, sedimentation, contaminant transport and environmental remediation. This study investigates the evolution of 6 stratified water columns created by differences in salinity, suspended minerals and the presence of a bottom heat source. We use acoustic wave reflection, photography, and both electrical conductivity and temperature profiles to track changes in stratification. Results show that multiple concurrent processes emerge across layers in otherwise quiescent water bodies. Dissimilar chemo-thermo conditions give rise to chemical and thermal diffusion, convection, and double-diffusion convection. When stratification involves suspended particles, interlayer processes include diffusiophoresis, flocculation/aggregation, sedimentation, osmosis, and chemo-consolidation; in this case, the specific surface and surface charge of suspended particles, and the salt concentration in contiguous layers determine aggregation-sedimentation-consolidation patterns. The interlayer transition zone acts as a high-pass filter that preferentially reflects low-frequency long-wavelength P-waves; invasive thermal and electrical conductivity probes provide complementary information and may identify stratification even when it is undetected by acoustic signals.

Where's Whaledo: A software toolkit for array localization of animal vocalizations.

Where's Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where's Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where's Whaledo in localizing groups of Ziphius cavirostris. We reconstruct the tracks of six individual animals vocalizing concurrently and identify Ziphius cavirostris tracks despite being obscured by a large pod of vocalizing dolphins.

Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

AbstractAcoustic emission is a nondestructive testing (NDT) technique, widely used to monitor the condition of structures for safety reasons especially in real time. The method utilizes the electrical signals generated by the elastic waves in a material under load to detect and locate damage in structures. However, identifying the sources of AE signals in concrete or composite materials can be challenging due to the anisotropic properties of materials and interpreting a large amount of AE data, leading to data misinterpretation and inaccurate detection of damage. Hence, the need for filtering out noise-induced signals from recorded data and emphasizing the actual AE source is crucial for monitoring and source localization of damage in real time. This study proposed a one-dimensional convolutional neural network (1D-CNN) deep learning approach to filter around 22,000 AE data in a reinforced concrete (RC) beam. The model utilizes significant AE parameters identified through neighborhood component analysis (NCA) to classify true AE signals from noise-induced signals. By using the optimized network parameters, a high classification accuracy of 97% and 96.29% was achieved during the training and testing phases, respectively. To check the reliability of the proposed AE filtering model in the real world, it was evaluated and verified using source location AE activities collected during a four-point bending test on a shear-deficient beam. The outcomes suggest that the proposed AE filtration model has the potential to accurately classify AE signals with an accuracy of 92.8% and proved that the filtration model provides accurate and valuable insight into source location determination.

Anthropogenic disturbance affects calling and collective behaviour in corvid roosts.

Acoustic communication plays an important role in coordinating group dynamics and collective movements across a range of taxa. However, anthropogenic disturbance can inhibit the production or reception of acoustic signals. Here, we investigate the effects of noise and light pollution on the calling and collective behaviour of wild jackdaws (Corvus monedula), a highly social corvid species that uses vocalizations to coordinate collective movements at winter roosting sites. Using audio and video monitoring of roosts in areas with differing degrees of urbanization, we evaluate the influence of anthropogenic disturbance on vocalizations and collective movements. We found that when levels of background noise were higher, jackdaws took longer to settle following arrival at the roost in the evening and also called more during the night, suggesting that human disturbance may cause sleep disruption. High levels of overnight calling were, in turn, linked to disruption of vocal consensus decision-making and less cohesive group departures in the morning. These results raise the possibility that, by affecting cognitive and perceptual processes, human activities may interfere with animals' ability to coordinate collective behaviour. Understanding links between anthropogenic disturbance, communication, cognition and collective behaviour must be an important research priority in our increasingly urbanized world. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Effects of Speech Characteristics on Electroglottographic and Instrumental Acoustic Voice Analysis Metrics in Women With Structural Dysphonia Before and After Treatment.

Literature suggests a dependency of the acoustic metrics, smoothed cepstral peak prominence (CPPS) and harmonics-to-noise ratio (HNR), on human voice loudness and fundamental frequency (F0). Even though this has been explained with different oscillatory patterns of the vocal folds, so far, it has not been specifically investigated. In the present work, the influence of three elicitation levels, calibrated sound pressure level (SPL), F0 and vowel on the electroglottographic (EGG) and time-differentiated EGG (dEGG) metrics hybrid open quotient (OQ), dEGG OQ and peak dEGG, as well as on the acoustic metrics CPPS and HNR, was examined, and their suitability for voice assessment was evaluated. In a retrospective study, 29 women with a mean age of 25 years (± 8.9, range: 18-53) diagnosed with structural vocal fold pathologies were examined before and after voice therapy or phonosurgery. Both acoustic and EGG signals were recorded simultaneously during the phonation of the sustained vowels /ɑ/, /i/, and /u/ at three elicited levels of loudness (soft/comfortable/loud) and unconstrained F0 conditions. A linear mixed-model analysis showed a significant effect of elicitation effort levels on peak dEGG, HNR, and CPPS (all p < .01). Calibrated SPL significantly influenced HNR and CPPS (both p < .01). Furthermore, F0 had a significant effect on peak dEGG and CPPS (p < .0001). All metrics showed significant changes with regard to vowel (all p < .05). However, the treatment had no effect on the examined metrics, regardless of the treatment type (surgery vs. voice therapy). The value of the investigated metrics for voice assessment purposes when sampled without sufficient control of SPL and F0 is limited, in that they are significantly influenced by the phonatory context, be it speech or elicited sustained vowels. Future studies should explore the diagnostic value of new data collation approaches such as voice mapping, which take SPL and F0 effects into account.

A Recognition and Classification Method for Underground Acoustic Emission Signals Based on Improved CELMD and Swin Transformer Neural Networks

To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission signals. This method leverages correlation coefficient filtering to extract the primary components, followed by classification and recognition using the Swin Transformer neural network. The results demonstrate that the improved CELMD method effectively extracts the main features of the acoustic emission signals with higher decomposition accuracy and reduced occurrences of mode mixing and end effects. Furthermore, the Swin Transformer neural network exhibits outstanding performance in classifying acoustic emission signals, surpassing both convolutional neural networks and ViT neural networks in terms of accuracy and convergence speed. Moreover, utilizing preprocessed data from the improved CELMD enhances the performance of the Swin Transformer neural network. With an increase in data volume, the accuracy, stability, and convergence speed of the Swin Transformer neural network continuously improve, and using preprocessed data from the enhanced CELMD yields superior training results compared to those obtained without preprocessing.

Acoustically Enhanced Triboelectric Stethoscope for Ultrasensitive Cardiac Sounds Sensing and Disease Diagnosis.

Electronic stethoscope used to detect cardiac sounds that contains essential clinical information is a primary tool for diagnosis of various cardiac disorders. However, the linear electro-mechanical constitutive relation makes conventional piezoelectric sensors rather ineffective to detect low-intensity, low-frequency heart acoustic signal without the assistance of complex filtering and amplification circuits. Herein, we find that triboelectric sensor features superior advantages over piezoelectric one for micro-quantity sensing originated from the fast saturated constitutive characteristic. As a result, the triboelectric sensor shows ultrahigh sensitivity (1215mV/Pa) than the piezoelectric counterpart (21mV/Pa) in the sound pressure range of 50 - 80dB under the same testing condition. By designing a trumpet-shaped auscultatory cavity with a power function cross-section to achieve acoustic energy converging and impedance matching, triboelectric stethoscope delivers 36dB signal-to-noise ratio for human test (2.3 times of that for piezoelectric one). Further combining with machine learning, five cardiac states can be diagnosed at 97% accuracy. In general, the triboelectric sensor is distinctly unique in basic mechanism, provides a novel design concept for sensing micromechanical quantities, and presents significant potential for application in cardiac sounds sensing and disease diagnosis. This article is protected by copyright. All rights reserved.

Experimental Study on Near-Wall Laser-Induced Cavitation Bubble Micro-Dimple Formation on 7050 Aluminum Alloy

To investigate the feasibility and formation laws of fabricating micro-dimples induced by near-wall laser-induced cavitation bubble (LICB) on 7050 aluminum alloy. A high-speed camera and a fiber-optic hydrophone system were used to capture pulsation evolution images and acoustic signals of LICB. Meanwhile, a three-dimensional profilometer was employed to examine the contour morphology of the surface micro-dimple on the specimen. The results show that at an energy level of 500 mJ, the total pulsation period for the empty bubble is 795 μs, with individual pulsation periods of 412.5 μs, 217 μs, and 165 μs for the first, second, and third cycles, respectively, with most energy of the laser and bubble being consumed during the first evolution period. Under the synergy of the plasma shock wave and collapse shock wave, a spherical dimple with a diameter of 450 μm is formed on the sample surface with copper foil as the absorption layer. A model of micro-dimple formed by LICB impact is established. As the energy increases, the depth of the surface micro-dimple peaks at an energy of 400 mJ and then decreases. The depth of the surface micro-dimple increases with the increase in the number of impacts; the optimal technology parameters for the micro-dimple formation by LICB impact are as follows: the absorption layer is copper foil, the energy is 400 mJ, and the number of impacts is three.

Production of sounds by squirrelfish during symbiotic relationships with cleaner wrasses

Examples of symbiotic relationships often include cleaning mutualisms, typically involving interactions between cleaner fish and other fish, called the clients. While these cleaners can cooperate by removing ectoparasites from their clients, they can also deceive by feeding on client mucus, a behavior usually referred to as “cheating behavior” that often leads to a discernible jolt from the client fish. Despite extensive studies of these interactions, most research has focused on the visual aspects of the communication. In this study, we aimed to explore the role of acoustic communication in the mutualistic relationship between cleaner fishes and nine holocentrid client species across four regions of the Indo-Pacific Ocean: French Polynesia, Guam, Seychelles, and the Philippines. Video cameras coupled with hydrophones were positioned at various locations on reefs housing Holocentridae fish to observe their acoustic behaviors during interactions. Our results indicate that all nine species of holocentrids can use acoustic signals to communicate to cleaner fish their refusal of the symbiotic interaction or their desire to terminate the cooperation. These sounds were predominantly observed during agonistic behavior and seem to support visual cues from the client. This study provides a novel example of acoustic communication during a symbiotic relationship in teleosts. Interestingly, these vocalizations often lacked a distinct pattern or structure. This contrasts with numerous other interspecific communication systems where clear and distinguishable signals are essential. This absence of a clear acoustic pattern may be because they are used in interspecific interactions to support visual behavior with no selective pressure for developing specific calls required in conspecific recognition. The different sound types produced could also be correlated with the severity of the client response. There is a need for further research into the effects of acoustic behaviors on the quality and dynamics of these mutualistic interactions.

Underwater Non-stationary Acoustic Signal Detection Based on the STHOC Noise Suppression

Underwater Non-stationary Acoustic Signal Detection Based on the STHOC Noise Suppression

Lipwatch: Enabling Silent Speech Recognition on Smartwatches using Acoustic Sensing

Silent Speech Interfaces (SSI) on mobile devices offer a privacy-friendly alternative to conventional voice input methods. Previous research has primarily focused on smartphones. In this paper, we introduce Lipwatch, a novel system that utilizes acoustic sensing techniques to enable SSI on smartwatches. Lipwatch leverages the inaudible waves emitted by the watch's speaker to capture lip movements and then analyzes the echo to enable SSI. In contrast to acoustic sensing-based SSI on smartphones, our development of Lipwatch takes into full consideration the specific scenarios and requirements associated with smartwatches. Firstly, we elaborate a wake-up-free mechanism, allowing users to interact without the need for a wake-up phrase or button presses. The mechanism utilizes the inertial sensors on the smartwatch to detect gestures, in combination with acoustic signals that detecting lip movements to determine whether SSI should be activated. Secondly, we design a flexible silent speech recognition mechanism that explores limited vocabulary recognition to comprehend a broader range of user commands, even those not present in the training dataset, relieving users from strict adherence to predefined commands. We evaluate Lipwatch on 15 individuals using a set of the 80 most common interaction commands on smartwatches. The system achieves a Word Error Rate (WER) of 13.7% in user-independent test. Even when users utter commands containing words absent in the training set, Lipwatch still demonstrates a remarkable 88.7% top-3 accuracy. We implement a real-time version of Lipwatch on a commercial smartwatch. The user study shows that Lipwatch can be a practical and promising option to enable SSI on smartwatches.

A pipeline corrosion detecting method using percussion and residual neural network

Corrosion of pipeline walls can lead to serious safety accidents such as leaks, fires and even explosions. This paper proposes a corrosion detection method using deep learning based on percussion sound for pipelines. The percussion induced acoustic signals are processed by wavelet threshold noise reduction and double threshold endpoint detection to generate the Mel spectrograms, and then an 18-layer residual network (ResNet18) is used to mine the depth information and classify the degree of pipeline corrosion. We conducted experiments to verify the validity of the approach. Seven working conditions are generated by electrochemical corrosion of a pipe specimen, and percussions are applied at five different positions under the same working conditions to collect the impact acoustic signals. The test results show that the method can quickly, efficiently and accurately detect the degree of pipeline corrosion, classify the degree of pipe corrosion without being affected by the striking position Therefore, the model has great potential for application in detecting the internal corrosion of pipelines based on percussion sounds.

Pushing the Limits of Acoustic Spatial Perception via Incident Angle Encoding

With the growing popularity of smart speakers, numerous novel acoustic sensing applications have been proposed for low-frequency human speech and high-frequency inaudible sounds. Spatial information plays a crucial role in these acoustic applications, enabling various location-based services. However, typically commercial microphone arrays face limitations in spatial perception of inaudible sounds due to their sparse array geometries optimized for low-frequency speech. In this paper, we introduce MetaAng, a system designed to augment microphone arrays by enabling wideband spatial perception across both speech signals and inaudible sounds by leveraging the spatial encoding capabilities of acoustic metasurfaces. Our design is grounded in the fact that, while sensitive to high-frequency signals, acoustic metasurfaces are almost non-responsive to low-frequency speech due to significant wavelength discrepancy. This observation allows us to integrate acoustic metasurfaces with sparse array geometry, simultaneously enhancing the spatial perception of high-frequency and low-frequency acoustic signals. To achieve this, we first utilize acoustic metasurfaces and a configuration optimization algorithm to encode the unique features for each incident angle. Then, we propose an unrolling soft thresholding network that employs neural-enhanced priors and compressive sensing for high-accuracy, high-resolution multi-source angle estimation. We implement a prototype, and experimental results demonstrate that MetaAng maintains robustness across various scenarios, facilitating multiple applications, including localization and tracking.

Afitness: Fitness Monitoring on Smart Devices via Acoustic Motion Images

Recently, as fitness has become a popular part of people’s lives, the intention to record fitness processes and assess the standards of fitness movements has grown increasingly keen. However, the existing approaches have some limitations, for example, wearable devices can hinder users’ fitness activities; computer vision–based solutions pose the risk of privacy breach, and so on. Fortunately, we observed that smartspeaker, acoustic-based sensing is a promising method of activity monitoring. In this article, we propose Afitness, an acoustic-based sensing system that enables non-intrusive, passive, and high-precision fitness detection. Afitness has the following three innovations. (i) We utilize pulse compression to generate high-precision motion distance images on commercial devices that can be visually recognized. (ii) We propose a data augmentation algorithm, which also incorporates transfer learning to greatly reduce the pressure of data collection. (iii) We exploit incremental learning techniques that allow Afitness to improve the portability of our system and recognize new actions. Overall, Afitness achieves acoustic signal interpretability and environmental reliability detection.

Acoustic logging array signal denoising using U-net and a case study in TangGu oil field

Abstract This study developed a noise-reduction method for acoustic logging array signals using a deep neural network algorithm in the time-frequency domain. Initially, we derived analytical solutions for the received waveforms when the acoustic logging tool was positioned either at the centre or eccentrically within the borehole. To simulate the received waveforms across various formations, we developed a real-axis integration algorithm. Subsequently, we devised a noise-reduction algorithm workflow based on a convolutional neural network (CNN) and configured the structure and parameters of the U-net using TensorFlow. To address the scarcity of open datasets, we established both signal and noise datasets. The signal dataset was generated using theoretical simulation encompassing various model parameters, while the noise dataset was collected during tool testing and downhole operations. The trained model demonstrated substantial noise-reduction capabilities during validation. To validate the effectiveness of the algorithm, we applied noise reduction to actual data collected during downhole operations in the TangGu oilfield, yielding impressive results across different types of noisy data. Therefore, the U-net-based time-domain noise-reduction algorithm proposed in this paper holds the potential to significantly improve the quality of acoustic logging array signals.