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

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.

KEYWORDS:

• Voice activity detection
• voyage data recorder
• acoustic feature
• residue network
• attention mechanism

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 61806178]; Postdoctoral Science Foundation of China [grant number 2020M680932]; Key Projects of National Natural Science Foundation of Zhejiang Province [grant number LY21F010015] and Fundamental Research Funds for the Central Universities [grant number 3132021226].