<figure><img src="image_1.png" /></figure>

HAL
open science

# AI-Powered Acoustic Diagnostics: Advancing Respiratory Health and Animal Welfare

Núria Mach, Ignasi Nou Plana, Marie Corbin, Mariette F. Ducatez, Gilles

Meyer, Maria Alsina Pagès Rosa, Antonio Velarde

► To cite this version:

Núria Mach, Ignasi Nou Plana, Marie Corbin, Mariette F. Ducatez, Gilles Meyer, et al. AI-Powered Acoustic Diagnostics: Advancing Respiratory Health and Animal Welfare. EAAP, Aug 2025, Innsbruck, Austria. 2025. [hal-05233337](https://hal.inrae.fr/hal-05233337v1)

HAL Id: hal-05233337

https://hal.inrae.fr/hal-05233337v1

Submitted on 1 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

Public Domain
# AI-Powered Acoustic Diagnostics: Advancing Respiratory Health and Animal Welfare

Núria Mach<fnref n="1" />*, Ignasi Nou Plana<fnref n="2" />, Marie Corbin<fnref n="1" />, Mariette Ducatez<fnref n="1" />, Gilles Meyer<fnref n="1" />, Rosa Maria Alsina Pagès<fnref n="2" />, Antonio Velarde<fnref n="3" />

## Abstract

Bovine Respiratory Disease presents a significant global challenge, necessitating innovative diagnostic solutions for early treatment to protect animal health and welfare. Acoustic biomarkers offer an exciting and non-invasive method for assessing respiratory functionality. In this study, 28 healthy, unweaned calves were divided into four groups to represent a range of respiratory dynamics: (1) antibiotic-treated and infected with influenza D virus (IDV), bovine coronavirus (BCoV), and *Mycoplasma bovis*; (2) non-antibiotic-treated but infected; (3) antibiotic-treated and non-infected; and (4) non-antibiotic-treated and non-infected. The animals were monitored for clinical symptoms from 15 days before infection to 21 days after infection. The study aimed to develop an AI-driven diagnostic tool that identifies cough sounds as early biomarkers for respiratory diseases using continuous acoustic data. Of the 2,728 hours of recordings collected (~7 hours per sensor per day), a preliminary subset was processed for feature extraction using Wav2Vec2 without extensive preprocessing. A HuBERT-based model (Is960) classified 1,045 cough and non-cough events from 86 hours of recordings, achieving an impressive 92% accuracy. Precision and recall values were 0.94/0.95 for non-coughs and 0.87/0.87 for coughs. To validate these results, a Gaussian Generalized Additive Model was applied to compare AI-derived cough scores with veterinary assessments of clinical symptoms. The analysis revealed no significant difference, demonstrating that the AI-powered approach is on par with veterinary evaluations. This method is non-invasive, cost-effective, and capable of providing autonomous real-time data, enabling prompt interventions to safeguard animal health and welfare.

<footnote n="1">¹IHAP, INRAE, ENVT, Université de Toulouse, Toulouse, France</footnote>

<footnote n="2">²HER – Human-Environment Research, La Salle – Ramon Llull University, Barcelona, Spain</footnote>

<footnote n="3">³IRTA, Animal Welfare, Monells, Spain</footnote>