Deep Learning Based Sound Event Detection and Classification

Monday, March 29, 2021 - 1:00pm to 2:00pm

DISSERTATION DEFENSE

Department of Computer Science and Engineering

University of South Carolina

Author : Alireza Nasiri

Advisor : Dr. Jianjun Hu

Date : March 29, 2021

Time : 1:00 - 3:00 pm

Place : Virtual Defense (link below)

Zoom link: https://us02web.zoom.us/j/83125251774?pwd=NDEwK3M4b3NuT0djQ25BMlQ2cGtuZz09

Abstract

Hearing sense has an important role in our daily lives. During the recent years, there has been many studies to transfer this capability to the computers. In this dissertation, we design and implement deep learning based algorithms to improve the ability of the computers in recognizing the different sound events.

In the first topic, we investigate sound event detection, which identifies the time boundaries of the sound events in addition to the type of the events. For sound event detection, we propose a new method, AudioMask, to benefit from the object-detection techniques in computer vision. In this method, we convert the question of identifying time boundaries for sound events, into the problem of identifying objects in images by treating the spectrograms of the sound as images. AudioMask first applies Mask R-CNN, an algorithm for detecting objects in images, to the log-scaled mel-spectrograms of the sound files. Then we use a frame-based sound event classifier trained independently from Mask R-CNN, to analyze each individual frame in the candidate segments. Our experiments show that, this approach has promising results and can successfully identify the exact time boundaries of the sound events.

In the second topic, we present SoundCLR, a supervised contrastive learning based method for effective environmental sound classification with state-of-the-art performance, which works by learning representations that disentangle the samples of each class from those of other classes. We also exploit transfer learning and strong data augmentation to improve the results. Our extensive benchmark experiments show that our hybrid deep network models trained with combined contrastive and cross-entropy loss achieved the state-of-the-art performance on three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of 99.75%, 93.4%, and 86.49% respectively. The ensemble version of our models also outperforms other top ensemble methods.

Finally, we analyze the acoustic emissions that are generated during the degradation process of SiC composites. The aim here is to identify the state of the degradation in the material, by classifying its emitted acoustic signals. As our baseline, we use random forest method on expert-defined features. Also we propose a deep neural network of convolutional layers to identify the patterns in the raw sound signals. Our experiments show that both of our methods are reliably capable of identifying the degradation state of the composite, and in average, the convolutional model significantly outperforms the random forest technique.