AI Medical Compendium Topic

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Vocalization, Animal

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Robust sound event detection in bioacoustic sensor networks.

PloS one
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection...

Convolutional neural network for detecting odontocete echolocation clicks.

The Journal of the Acoustical Society of America
In this work, a convolutional neural network based method is proposed to automatically detect odontocetes echolocation clicks by analyzing acoustic data recordings from a passive acoustic monitoring system. The neural network was trained to distingui...

DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Rodents engage in social communication through a rich repertoire of ultrasonic vocalizations (USVs). Recording and analysis of USVs has broad utility during diverse behavioral tests and can be performed noninvasively in almost any rodent behavioral m...

Deep convolutional network for animal sound classification and source attribution using dual audio recordings.

The Journal of the Acoustical Society of America
This paper introduces an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size ...

Quantifying ultrasonic mouse vocalizations using acoustic analysis in a supervised statistical machine learning framework.

Scientific reports
Examination of rodent vocalizations in experimental conditions can yield valuable insights into how disease manifests and progresses over time. It can also be used as an index of social interest, motivation, emotional development or motor function de...

Comparing context-dependent call sequences employing machine learning methods: an indication of syntactic structure of greater horseshoe bats.

The Journal of experimental biology
For analysis of vocal syntax, accurate classification of call sequence structures in different behavioural contexts is essential. However, an effective, intelligent program for classifying call sequences from numerous recorded sound files is still la...

Learning to localize sounds in a highly reverberant environment: Machine-learning tracking of dolphin whistle-like sounds in a pool.

PloS one
Tracking the origin of propagating wave signals in an environment with complex reflective surfaces is, in its full generality, a nearly intractable problem which has engendered multiple domain-specific literatures. We posit that, if the environment a...

Assessment of Laying Hens' Thermal Comfort Using Sound Technology.

Sensors (Basel, Switzerland)
Heat stress is one of the most important environmental stressors facing poultry production and welfare worldwide. The detrimental effects of heat stress on poultry range from reduced growth and egg production to impaired health. Animal vocalisations ...

Statistical learning of transition patterns in the songbird auditory forebrain.

Scientific reports
Statistical learning of transition patterns between sounds-a striking capability of the auditory system-plays an essential role in animals' survival (e.g., detect deviant sounds that signal danger). However, the neural mechanisms underlying this capa...

Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires.

PLoS computational biology
Animals produce vocalizations that range in complexity from a single repeated call to hundreds of unique vocal elements patterned in sequences unfolding over hours. Characterizing complex vocalizations can require considerable effort and a deep intui...