Feature Extraction Based on the Non-Negative Matrix Factorization of Convolutional Neural Networks for Monitoring Domestic Activity With Acoustic Signals

In this paper, a feature extraction method is proposed based on the non-negative matrix factorization (NMF) for classifiers for monitoring domestic activities with acoustic signals. Most of the classifiers of the acoustic signals use data-independent spectral features (e.g., log-Mel spectrum and Mel-frequency cepstral coefficients). Recently, some novel feature extraction methods have been researched, including convolution-NMF-based features combined with K-means clustering. This study proposes an enhanced NMF-based feature extraction method that is inspired by the NMF-based noise reduction algorithm. The proposed method independently estimates the frequency basis matrix for each class, and then cascades the basis matrices to form the entire frequency bases, where the acoustic signal is transformed to the proposed feature by estimating the temporal basis matrix with the trained frequency bases. In addition, this study proposes a data augmentation method for the proposed feature that is inspired by the “mix and shuffle” method for audio waveforms. In order to evaluate the proposed system, which consists of the proposed NMF-based feature and the convolutional-neural-network-based classifier, some evaluations were performed using the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 Task 5 - Monitoring of Domestic Activities Based on Multi-channel Acoustics - Database. The results showed that the proposed system has comparable performance to that of state-of-the-art algorithms and that it has enhanced the F1-score performance of 6%-12% in comparison with the conventional NMF-based feature extraction method that is based on convolutional NMF and K-means clustering.