Detection and localization of sound events based on principal components analysis
Min Yuan, Pengli Xin, Chundong Xu, Hao Liu · 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE) · 2022
In order to improve the accuracy of sound source localization based on depth learning and reduce the amount of model calculation. In this paper, a principal component analysis sound event localization and detection network (PCA-SELDnet) based on convolution recurrent network model (SELDnet) is proposed, which introduces time domain feature weighted phase transition generalized cross-correlation (GCC-PHAT) and uses principal component analysis (PCA) carries out dimensionality reduction and reconstruction, obtains the features of several dimensions with large contribution, and then combines them with the amplitude spectrum of the original spectrogram for input. The combined features are mapped into two output matrices through SELDnet model, one output is sound event detection matrix and the other output is sound source localization matrix. The results show that PCA-SELDnet improves the accuracy of sound event localization.