A Comparative Analysis of Denoising Methods for Deep Learning-Based Audio Event Detection in Noisy Agricultural Environments - Moreira Souza, André; Moreira, Guilherme Augusto; Pulcinelli, Lucas Eduardo Gulka (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2025: Anais do XL Simpósio Brasileiro de Bancos de Dados, 2025)
This study investigates whether traditional denoising improves deep learning for Audio Event Detection (AED) in real-world, noisy environments like livestock farms. We evaluated three denoising algorithms — Spectral Subtraction, Adaptive Kalman Filter, and SD-ROM — on the aSwine dataset using state-of-the-art models, including Pretrained Audio Neural Networks (PANNs) and the Audio Spectrogram Transformer (AST). Contrary to conventional wisdom, all denoising methods proved detrimental, with a worst-case scenario showing a 65.8% mean Average Precision (mAP) decrease. We conclude that powerful models learn to be inherently noise-robust, making robust architectures a superior strategy to noise preprocessing.