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Many previous audio-visual voice-related works focus on speech, ignoring thesinging voice in the growing number of musical video streams on the Internet.For processing diverse musical video data, voice activity detection is anecessary step. This paper attempts to detect the speech and singing voices oftarget performers in musical video streams using audiovisual information. Tointegrate information of audio and visual modalities, a multi-branch network isproposed to learn audio and image representations, and the representations arefused by attention based on semantic similarity to shape the acousticrepresentations through the probability of anchor vocalization. Experimentsshow the proposed audio-visual multi-branch network far outperforms theaudio-only model in challenging acoustic environments, indicating thecross-modal information fusion based on semantic correlation is sensible andsuccessful.

Attention-based cross-modal fusion for audio-visual voice activity detection in musical video streams