Training Set Synthesis for Bioacoustic Denoising: A Case Study With Mice

2026-08-10

AI Summary

This study addresses the challenge of denoising bioacoustic recordings corrupted by environmental noise, where clean reference signals are typically unavailable for supervised training. To circumvent the need for real clean data, the authors propose a self-supervised approach that synthesizes training samples containing fundamental frequency and harmonic ridges. They develop a U-Net-based model to predict complex ratio masks and introduce a ridge-guided weighted loss function to better preserve fine-grained vocal structure during denoising. Evaluated on murine ultrasonic vocalizations, the method significantly improves the accuracy of fundamental frequency and harmonic tracking, enhances scale-invariant signal-to-noise ratio, and boosts the generalization performance of downstream classifiers in noisy field conditions.

Abstract

Bioacoustic recordings are often degraded by ambient noise, which complicates the analysis of weak or noise-overlapped vocalizations. Convolutional neural networks, particularly U-Net architectures, have shown a strong denoising performance in speech and music processing. However, their direct application to bioacoustic signals is limited by the scarcity of clean training data. To address this issue, we propose a training set synthesis approach and develop a supervised denoising model that predicts a complex ratio mask in the time-frequency domain. The model leverages ridges, or frequency contours, that represent the fundamental frequency together with one or more harmonic partial components of vocalizations. These ridges are used both for the synthesis of training sets and to design a loss function that assigns higher weights to the ridge regions (ridge-guided loss function). This weighting step helps the network better preserve vocalization details during denoising. As a case study, we evaluate our approach using ultrasonic vocalizations (USVs) recordings of house mice, which are widely studied in behavioral biology and neuroscience. In actual field recordings, the proposed method enhances fundamental and harmonic partial ridge tracking compared to our previous signal-processing approach. In addition, a classifier trained on denoised data improves USV classification on out-of-sample, noisy recordings from wild and domesticated mice compared to classifiers trained on noisy recordings. Our proposed method also substantially improves the scale-invariant signal-to-distortion ratio on synthetic testing data across a wide range of input signal-to-noise ratios. Although we focus on USVs, the proposed approach should be broadly applicable to other bioacoustic signals with trackable ridges, and thus enables ridgebased training set synthesis and denoising.

Problem

• bioacoustic denoising
• training data scarcity
• ultrasonic vocalizations
• ambient noise
• vocalization analysis

Innovation

• training set synthesis
• ridge-guided loss
• bioacoustic denoising
• complex ratio mask
• ultrasonic vocalizations