AIMC Topic: Acoustics

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Listening deeper: neural networks unravel acoustic features in preterm infant crying.

Scientific reports
Early infant crying provides critical insights into neurodevelopment, with atypical acoustic features linked to conditions such as preterm birth. However, previous studies have focused on limited and specific acoustic features, hindering a more compr...

Hidden Markov model for acoustic pesticide exposure detection and hive identification in stingless bees.

PloS one
Pollinator populations are declining globally at an unprecedented rate, driven by factors such as pathogens, habitat loss, climate change, and the widespread application of pesticides. Although colony losses remain difficult to prevent, precision bee...

Optimal feature selection and model explanation for reef fish sound classification.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Fish produce a wide variety of sounds that contribute to the soundscapes of aquatic environments. In reef systems, these sounds are important acoustic cues for various ecological processes. Artificial intelligence methods to detect, classify and iden...

Acoustic monitoring for tropical insect conservation.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Monitoring the species-specific sounds produced by insects could provide us with a rapid, reliable, non-invasive measure of tropical ecosystem health and biodiversity. Although acoustic biodiversity monitoring has made rapid progress over the past de...

Decoding Poultry Welfare from Sound-A Machine Learning Framework for Non-Invasive Acoustic Monitoring.

Sensors (Basel, Switzerland)
Acoustic monitoring presents a promising, non-invasive modality for assessing animal welfare in precision livestock farming. In poultry, vocalizations encode biologically relevant cues linked to health status, behavioral states, and environmental str...

Unlocking the soundscape of coral reefs with artificial intelligence: pretrained networks and unsupervised learning win out.

PLoS computational biology
Passive acoustic monitoring can offer insights into the state of coral reef ecosystems at low-costs and over extended temporal periods. Comparison of whole soundscape properties can rapidly deliver broad insights from acoustic data, in contrast to de...

Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations.

Sensors (Basel, Switzerland)
Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous d...

Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach.

Journal of medical Internet research
BACKGROUND: Crisis hotlines serve as a crucial avenue for the early identification of suicide risk, which is of paramount importance for suicide prevention and intervention. However, assessing the risk of callers in the crisis hotline context is cons...

Explainable classification of goat vocalizations using convolutional neural networks.

PloS one
Efficient precision livestock farming relies on having timely access to data and information that accurately describes both the animals and their surrounding environment. This paper advances classification of goat vocalizations leveraging a publicly ...

Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling.

Scientific reports
Embarrassment is a social emotion that shares many characteristics with social anxiety (SA). Most people experience embarrassment in their daily lives, but it is quite overlooked in research. We characterized embarrassment through an interdisciplinar...