Voice activity detection for audio signal of voyage data recorder using residue network and attention mechanism

Abstract

ABSTRACT In this paper, a voice activity detection (VAD) method based on residue network and attention mechanism for VDR audio signal is proposed. First, several frame-wise acoustic features are extracted to eliminate audio data amount. Then, the frame-wise features are reshaped to construct 2-D feature maps. To facilitate parameter optimisation, a residue network is utilised herein to learn the complex mapping function from acoustic features to VAD output. Furthermore, to focus more on local information and suppress useless data, an attention mechanism is combined with the residue network to extract complex hidden information from the 2-D feature maps. Finally, the compressed 2-D features are flattened and refined by a dense layer to cover global information. The proposed method can automatically learn the mapping function efficiently and effectively. Experimental results show that the proposed method achieves the highest AUC, and the second highest ACC and F-measure compared with reference methods on the annotated real-world VDR audio dataset.