Deep Learning Based Sound Event Detection and Classification

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. The code for this study is available at https://github.com/alireza-nasiri/AudioMask. 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

High-temperature ceramic matrix composites prepared via microwave energy enhanced chemical vapour infiltration

SiC fibre reinforced SiC composites must be >90% dense in order to offer a superior structural material alternative to current systems used in aerospace engines. To achieve this the use of microwave energy enhanced chemical vapour infiltration (MCVI) has been investigated as a possible faster and more energy efficient manufacturing route. Key processing parameters were identified and their effects on the rate of deposition of SiC and the composite’s microstructure were assessed using a suite of characterisation techniques. The rate of SiC deposition had an Arrhenius relationship with the temperature and the use of microwaves is thought to have also lowered the activation energy of the decomposition reaction. The fundamental benefit of this advanced processing method was the inverse thermal gradient produced by using microwave energy. This was studied both experimentally and via finite-difference time-domain modelling (FDTD). The latter showed a direct correlation between susceptor size and microwave absorption. A SiC slurry calendaring impregnation route was also developed to fill macro porosity in the SiC fibre preform. A number of slurries were characterised to find a suitable slurry composition that reduced the total volume of porosity in the preform to further reduce CVI processing time

Reports required by government auditing standards and the uniform guidance for the year ended June 30, 2019

This is an audit of the University of South Carolina's compliance with the types of compliance requirements described in the OMB Compliance Supplement that could have a direct and material effect on each of the University’s major federal programs. The University’s major federal programs are identified in the summary of auditor’s results section of the accompanying schedule of findings and questioned costs