Embryonic Exposure to VPA Influences Chick Vocalisations: A Computational Study

2026-01-18

AI Summary

Traditional manual annotation struggles to objectively and efficiently capture the complexity of chick vocalizations and lacks quantitative tools to assess vocal development abnormalities induced by embryonic valproic acid (VPA) exposure. This study proposes a computational framework integrating automatic sound event detection, multidimensional acoustic feature extraction (time-domain, frequency-domain, and energy-domain), and unsupervised clustering (e.g., Gaussian Mixture Models) to enable high-throughput, objective analysis of chick vocal repertoires for the first time. The approach reveals that VPA exposure significantly alters the acoustic structure of specific call subtypes, characterized by shortened duration, reduced pitch variability, and shifted energy distribution—effects most pronounced in loud calls. These findings establish a novel paradigm for investigating early communication deficits in autism-like animal models.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Speech
• Search and Optimization: Evolutionary Computation

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

In young animals like poultry chicks (Gallus gallus), vocalisations convey information about affective and behavioural states. Traditional approaches to vocalisation analysis, relying on manual annotation and predefined categories, introduce biases, limit scalability, and fail to capture the full complexity of vocal repertoires. We introduce a computational framework for the automated detection, acoustic feature extraction, and unsupervised learning of chick vocalisations. Applying this framework to a dataset of newly hatched chicks, we identified two primary vocal clusters. We then tested our computational framework on an independent dataset of chicks exposed during embryonic development to vehicle or Valproic Acid (VPA), a compound that disrupts neural development and is linked to autistic-like symptoms. Clustering analysis on the experimental dataset confirmed two primary vocal clusters and revealed systematic differences between groups. VPA-exposed chicks showed an altered repertoire, with a relative increase in softer calls. VPA differentially affected call clusters, modulating temporal, frequency, and energy domain features. Overall, VPA-exposed chicks produced vocalisations with shorter duration, reduced pitch variability, and modified energy profiles, with the strongest alterations observed in louder calls. This study provides a computational framework for analysing animal vocalisations, advancing knowledge of early-life communication in typical and atypical vocal development.

Problem

• vocalisation analysis
• embryonic exposure
• Valproic Acid
• neural development
• animal communication

Innovation

• computational framework
• unsupervised learning
• vocalisation analysis
• Valproic Acid (VPA)
• acoustic feature extraction