Optimizing Spectrogram Resolution and Training Strategies for Real-Time Killer Whale Call Type Classification

摘要

Automated identification of killer whale call types from continuous acoustic recordings is essential for scalable population monitoring, yet existing general-purpose frameworks such as ANIMAL-SPOT suffer from suboptimal spectrogram resolution and lack strategies tailored to the spectral-temporal characteristics of killer whale vocalizations. We identify two key limitations of the ANIMAL-SPOT framework: (1)~no possibility to choose different architecture of CNN backbone, and (2)~the absence of regularization and class-balancing techniques limits generalization across call types with varying abundance. To address these issues, we propose a framework that combines optimized STFT parameters (FFT size 1024, hop length 172) with label smoothing and targeted oversampling, evaluated across three CNN backbones and five segment lengths. On a dataset of 12 killer whale vocalization classes from Avacha Gulf, Russia, our best configuration (ResNet-18 with 1200\,ms segments) achieves \textbf{97.1\%} segment-level accuracy, compared to \textbf{96.2\%} for the ANIMAL-SPOT baseline --- a relative error reduction of 23.7\%. We further present an interactive demonstration system for uploading recordings and obtaining time-resolved call-type predictions, enabling rapid analysis of passive acoustic monitoring data. Demo video link: https://drive.google.com/file/d/1ITA_52WdnAcyg7zRjIIshutjl6Lp_um1/view?usp=drive_link Presentation slides: https://drive.google.com/file/d/1sd-JH1R5YPa9iRtsbRO-OZxY8-fyglsO/view?usp=drive_link