scholarly journals Fast and accurate annotation of acoustic signals with deep neural networks

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Elsa Steinfath ◽  
Adrian Palacios-Muñoz ◽  
Julian R Rottschäfer ◽  
Deniz Yuezak ◽  
Jan Clemens
Keyword(s):  
Graphical User Interface ◽  
Acoustic Communication ◽  
Deep Neural Networks ◽  
Acoustic Signals ◽  
Low Latency ◽  
Manual Annotation ◽  
Animal Kingdom ◽  
Communication Signals ◽  
Sound Pulses ◽  
Robust To Noise

Acoustic signals serve communication within and across species throughout the animal kingdom. Studying the genetics, evolution, and neurobiology of acoustic communication requires annotating acoustic signals: segmenting and identifying individual acoustic elements like syllables or sound pulses. To be useful, annotations need to be accurate, robust to noise, and fast.We here introduce DeepAudioSegmenter (DAS), a method that annotates acoustic signals across species based on a deep-learning derived hierarchical presentation of sound. We demonstrate the accuracy, robustness, and speed of DAS using acoustic signals with diverse characteristics from insects, birds, and mammals. DAS comes with a graphical user interface for annotating song, training the network, and for generating and proofreading annotations. The method can be trained to annotate signals from new species with little manual annotation and can be combined with unsupervised methods to discover novel signal types. DAS annotates song with high throughput and low latency for experimental interventions in realtime. Overall, DAS is a universal, versatile, and accessible tool for annotating acoustic communication signals.

scholarly journals Fast and accurate annotation of acoustic signals with deep neural networks

2021 ◽  
Author(s):  
Elsa Steinfath ◽  
Adrian Palacios ◽  
Julian Rottschaefer ◽  
Deniz Yuezak ◽  
Jan Clemens
Keyword(s):  
Graphical User Interface ◽  
Deep Neural Networks ◽  
Closed Loop ◽  
Acoustic Signals ◽  
Courtship Song ◽  
Ultrasonic Vocalizations ◽  
Manual Annotation ◽  
Animal Kingdom ◽  
Sound Pulses ◽  
Robust To Noise

Acoustic signals serve communication within and across species throughout the animal kingdom. Studying the genetics, evolution, and neurobiology of acoustic communication requires annotating acoustic signals: segmenting and identifying individual acoustic elements like syllables or sound pulses. To be useful, annotations need to be accurate, robust to noise, fast. We introduce DeepSS, a method that annotates acoustic signals across species based on a deep-learning derived hierarchical presentation of sound. We demonstrate the accuracy, robustness, and speed of DeepSS using acoustic signals with diverse characteristics: courtship song from flies, ultrasonic vocalizations of mice, and syllables with complex spectrotemporal structure from birds. DeepSS comes with a graphical user interface for annotating song, training the network, and for generating and proofreading annotations (available at https://janclemenslab.org/deepss). The method can be trained to annotate signals from new species with little manual annotation and can be combined with unsupervised methods to discover novel signal types. DeepSS annotates song with high throughput and low latency, allowing realtime annotations for closed-loop experimental interventions.

scholarly journals Acoustic communication and the evolution of hearing in fishes

2000 ◽  
Vol 355 (1401) ◽  
pp. 1285-1288 ◽  
Author(s):  
Friedrich Ladich
Keyword(s):  
Inner Ear ◽  
Acoustic Communication ◽  
Acoustic Signals ◽  
Spectral Content ◽  
Auditory Thresholds ◽  
Selective Pressures ◽  
Vocal Signals ◽  
Species Specific ◽  
Hearing Abilities ◽  
Dominant Frequencies

Fishes have evolved a diversity of sound–generating organs and acoustic signals of various temporal and spectral content. Additionally, representatives of many teleost families such as otophysines, anabantoids, mormyrids and holocentrids possess accessory structures that enhance hearing abilities by acoustically coupling air–filled cavities to the inner ear. Contrary to the accessory hearing structures such as Weberian ossicles in otophysines and suprabranchial chambers in anabantoids, sonic organs do not occur in all members of these taxa. Comparison of audiograms among nine representatives of seven otophysan families from four orders revealed major differences in auditory sensitivity, especially at higher frequencies (> 1kHz) where thresholds differed by up to 50 dB. These differences showed no apparent correspondence to the ability to produce sounds (vocal versus non–vocal species) or to the spectral content of species–specific sounds. In anabantoids, the lowest auditory thresholds were found in the blue gourami Trichogaster trichopterus , a species not thought to be vocal. Dominant frequencies of sounds corresponded with optimal hearing bandwidth in two out of three vocalizing species. Based on these results, it is concluded that the selective pressures involved in the evolution of accessory hearing structures and in the design of vocal signals were other than those serving to optimize acoustic communication.

Mate call as reward: Acoustic communication signals can acquire positive reinforcing values during adulthood in female zebra finches (Taeniopygia guttata).

2016 ◽  
Vol 130 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Alexandra M. Hernandez ◽  
Emilie C. Perez ◽  
Hervé Mulard ◽  
Nicolas Mathevon ◽  
Clémentine Vignal
Keyword(s):  
Acoustic Communication ◽  
Taeniopygia Guttata ◽  
Zebra Finches ◽  
Communication Signals

scholarly journals Vocal production learning in the pale spear-nosed bat, Phyllostomus discolor

2020 ◽  
Vol 16 (4) ◽  
pp. 20190928 ◽  
Author(s):  
Ella Z. Lattenkamp ◽  
Sonja C. Vernes ◽  
Lutz Wiegrebe
Keyword(s):  
Mammalian Species ◽  
Vocal Learning ◽  
Acoustic Signals ◽  
Auditory Target ◽  
Animal Kingdom ◽  
Vocal Production ◽  
Learning Capacity ◽  
Directional Change ◽  
Set Up ◽  
Phyllostomus Discolor

Vocal production learning (VPL), or the ability to modify vocalizations through the imitation of sounds, is a rare trait in the animal kingdom. While humans are exceptional vocal learners, few other mammalian species share this trait. Owing to their singular ecology and lifestyle, bats are highly specialized for the precise emission and reception of acoustic signals. This specialization makes them ideal candidates for the study of vocal learning, and several bat species have previously shown evidence supportive of vocal learning. Here we use a sophisticated automated set-up and a contingency training paradigm to explore the vocal learning capacity of pale spear-nosed bats. We show that these bats are capable of directional change of the fundamental frequency of their calls according to an auditory target. With this study, we further highlight the importance of bats for the study of vocal learning and provide evidence for the VPL capacity of the pale spear-nosed bat.

scholarly journals Representation of Acoustic Communication Signals by Insect Auditory Receptor Neurons

2001 ◽  
Vol 21 (9) ◽  
pp. 3215-3227 ◽  
Author(s):  
Christian K. Machens ◽  
Martin B. Stemmler ◽  
Petra Prinz ◽  
Rüdiger Krahe ◽  
Bernhard Ronacher ◽  
...  
Keyword(s):  
Acoustic Communication ◽  
Communication Signals ◽  
Auditory Receptor ◽  
Receptor Neurons

scholarly journals Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3530
Author(s):  
Juan Parras ◽  
Santiago Zazo ◽  
Iván A. Pérez-Álvarez ◽  
José Luis Sanz González
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Acoustic Signals ◽  
Model Free ◽  
Underwater Environment ◽  
The Neural Network ◽  
Anchor Nodes ◽  
Localization Precision ◽  
Significant Effort ◽  
Localization Systems

In recent years, there has been a significant effort towards developing localization systems in the underwater medium, with current methods relying on anchor nodes, explicitly modeling the underwater channel or cooperation from the target. Lately, there has also been some work on using the approximation capabilities of Deep Neural Networks in order to address this problem. In this work, we study how the localization precision of using Deep Neural Networks is affected by the variability of the channel, the noise level at the receiver, the number of neurons of the neural network and the utilization of the power or the covariance of the received acoustic signals. Our study shows that using deep neural networks is a valid approach when the channel variability is low, which opens the door to further research in such localization methods for the underwater environment.

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