AIMC Topic: Vocalization, Animal

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Deep audio embeddings for vocalisation clustering.

PloS one
The study of non-human animals' communication systems generally relies on the transcription of vocal sequences using a finite set of discrete units. This set is referred to as a vocal repertoire, which is specific to a species or a sub-group of a spe...

Automated identification of chicken distress vocalizations using deep learning models.

Journal of the Royal Society, Interface
The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an 'iceberg indicator' of chicken welfa...

Computational bioacoustics with deep learning: a review and roadmap.

PeerJ
Animal vocalisations and natural soundscapes are fascinating objects of study, and contain valuable evidence about animal behaviours, populations and ecosystems. They are studied in bioacoustics and ecoacoustics, with signal processing and analysis a...

Design of a robotic zebra finch for experimental studies on developmental song learning.

The Journal of experimental biology
Birdsong learning has been consolidated as the model system of choice for exploring the biological substrates of vocal learning. In the zebra finch (Taeniopygia guttata), only males sing and they develop their song during a sensitive period in early ...

Automated annotation of birdsong with a neural network that segments spectrograms.

eLife
Songbirds provide a powerful model system for studying sensory-motor learning. However, many analyses of birdsong require time-consuming, manual annotation of its elements, called syllables. Automated methods for annotation have been proposed, but th...

Measuring context dependency in birdsong using artificial neural networks.

PLoS computational biology
Context dependency is a key feature in sequential structures of human language, which requires reference between words far apart in the produced sequence. Assessing how long the past context has an effect on the current status provides crucial inform...

BioCPPNet: automatic bioacoustic source separation with deep neural networks.

Scientific reports
We introduce the Bioacoustic Cocktail Party Problem Network (BioCPPNet), a lightweight, modular, and robust U-Net-based machine learning architecture optimized for bioacoustic source separation across diverse biological taxa. Employing learnable or h...

Function of a multimodal signal: A multiple hypothesis test using a robot frog.

The Journal of animal ecology
Multimodal communication may evolve because different signals may convey information about the signaller (content-based selection), increase efficacy of signal processing or transmission through the environment (efficacy-based selection), or modify t...

Comparing recurrent convolutional neural networks for large scale bird species classification.

Scientific reports
We present a deep learning approach towards the large-scale prediction and analysis of bird acoustics from 100 different bird species. We use spectrograms constructed on bird audio recordings from the Cornell Bird Challenge (CBC)2020 dataset, which i...

Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture.

Scientific reports
The use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the r...