Robust sound event classification by using denoising autoencoder
Jianchao Zhou, Liqun Peng, Xiaoou Chen, Deshun Yang · 2016
Over the last decade, a lot of research has been done on sound event classification. But a main problem with sound event classification is that the performance sharply degrades in the presence of noise. As spectrogram-based image features and denoising auto encoder reportedly have superior performance in noisy conditions, this paper proposes a new robust feature called denoising auto encoder image feature (DIF) for sound event classification which is an image feature extracted from an image-like representation produced by denoising auto encoder. Performance of the feature is evaluated by a classification experiment using a SVM classifier on audio examples with different noise levels, and compared with that of baseline features including mel-frequency cepstral coefficients (MFCC) and spectrogram image feature. The proposed DIF demonstrates better performance under noise-corrupted conditions.