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Robust Deep Learning Frameworks for Acoustic Scene and Respiratory Sound Classification
[article]
2021
arXiv
pre-print
This thesis focuses on dealing with the task of acoustic scene classification (ASC), and then applied the techniques developed for ASC to a real-life application of detecting respiratory disease. To deal with ASC challenges, this thesis addresses three main factors that directly affect the performance of an ASC system. Firstly, this thesis explores input features by making use of multiple spectrograms (log-mel, Gamma, and CQT) for low-level feature extraction to tackle the issue of
arXiv:2107.09268v1
fatcat:u62axiqr3bb5rn2qim3xcvjb6y