Exploring Regression-based Approach for Sound Event Detection in Noisy Environments

Abstract

Sound-event detection enables machines to detect when a particular sound event has occurred in addition to classifying the type of event. Successful detection of various sound events is paramount in building secure surveillance systems and other smart home appliances. However, noisy events and environ-ments exacerbate the performance of many sound event detection models, rendering them ineffective in real-world scenarios. Hence, the need for robust sound event detection algorithms in noisy environments with low inference times arises. You Only Hear Once (YOHO) is a purely convolutional architecture that uses a regression-based approach for sound-event-detection instead of the more common, frame-wise classification-based approach. The YOHO architecture proved robust in noisy environments, outperforming convolutional recurrent neural networks popular in sound event detection systems. Additionally, different ways to enhance the performance of the YOHO architecture are explored, experimenting with different computer vision architectures, dy-namic convolutional layers, pretrained audio neural networks and data augmentation methods to help improve the performance of the models on noisy data. Amongst several modifications to the YOHO architecture, the Frequency Dynamic Convolution Layers helped improve the internal model data representations by enforcing frequency-dependent convolution operations, which helped improve YOHO performance on noisy audios in outdoor and vehicular environments. Similarly, the FilterAugment data augmentation method and Convolutional Block Attention Module helped improve YOHO’s performance on the VOICe dataset containing noisy audios by augmenting the data and improving internal model representations of the input audio data using attention, respectively.