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arXiv · 2303.07816

Multi-Channel Masking with Learnable Filterbank for Sound Source Separation

Wang Dai·Archontis Politis·Tuomas Virtanen
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Abstract

This work proposes a learnable filterbank based on a multi-channel masking framework for multi-channel source separation. The learnable filterbank is a 1D Conv layer, which transforms the raw waveform into a 2D representation. In contrast to the conventional single-channel masking method, we estimate a mask for each individual microphone channel. The estimated masks are then applied to the transformed waveform representation like in the traditional filter-and-sum beamforming operation. Specifically, each mask is used to multiply the corresponding channel's 2D representation, and the masked output of all channels are then summed. At last, a 1D transposed Conv layer is used to convert the summed masked signal into the waveform domain. The experimental results show our method outperforms single-channel masking with a learnable filterbank and can outperform multi-channel complex masking with STFT complex spectrum in the STGCSEN model if a learnable filterbank is transformed to a higher feature dimension. The spatial response analysis also verifies that multi-channel masking in the learnable filterbank domain has spatial selectivity.

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Wang Dai, Archontis Politis, Tuomas Virtanen. 2023-03-14. Multi-Channel Masking with Learnable Filterbank for Sound Source Separation. https://arxiv.org/abs/2303.07816

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