SW-WAVENET: Learning Representation from Spectrogram and Wavegram Using Wavenet for Anomalous Sound Detection
Haihui Chen, Likai Ran, Xixia Sun, Chao Cai
Abstract
Anomalous Sound Detection (ASD) aims to identify whether the sound emitted from a machine is anomalous or not. Most advanced methods use 2-D CNNs to extract features of normal sounds from log-mel spectrograms for ASD. However, these methods can not fully exploit temporal information of log-mel spectrograms, resulting in poor performance on some machine types. In this paper, we propose a new framework for ASD named Spectrogram-Wavegram WaveNet (SW-WaveNet), which segments the 2-D log-mel spectrogram into 1-D waveform signals of different frequency bands and combines the representation vector extracted by WaveNet from segmented log-mel spectrograms and Wavegrams, respectively. The proposed framework utilizes WaveNet’s powerful capability of modeling waveform signals to effectively extract temporal information from log-mel spectrograms and Wavegrams. Experiments on the DCASE 2020 Challenge Task 2 dataset show that our framework achieves higher average AUC scores (93.25%) and pAUC scores (87.41%) than the previous works.