Robust Sound Event Classification by Using Denoising Autoencoder

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 autoencoder reportedly have superior performance in noisy conditions, this paper proposes a new robust feature called denoising autoencoder image feature (DIF) for sound event classification which is an image feature extracted from an image-like representation produced by denoising autoencoder. 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.