Scaling strategies for on-device low-complexity source separation with Conv-Tasnet

• URL: http://arxiv.org/abs/2303.03005v1
• Date: Mon, 6 Mar 2023 10:15:14 GMT
• Title: Scaling strategies for on-device low-complexity source separation with Conv-Tasnet
• Authors: Mohamed Nabih Ali, Francesco Paissan, Daniele Falavigna, Alessio Brutti
• Abstract summary: Several very effective neural approaches for single-channel speech separation have been presented in the literature. Due to the size and complexity of these models, their use on low-resource devices, e.g. for hearing aids, and earphones, is still a challenge. We consider three parameters that directly control the overall size of the model, namely: the number of residual blocks, the number of repetitions of the separation blocks and the number of channels in the depth-wise convolutions.
• Score: 8.40565031143262
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recently, several very effective neural approaches for single-channel speech separation have been presented in the literature. However, due to the size and complexity of these models, their use on low-resource devices, e.g. for hearing aids, and earphones, is still a challenge and established solutions are not available yet. Although approaches based on either pruning or compressing neural models have been proposed, the design of a model architecture suitable for a certain application domain often requires heuristic procedures not easily portable to different low-resource platforms. Given the modular nature of the well-known Conv-Tasnet speech separation architecture, in this paper we consider three parameters that directly control the overall size of the model, namely: the number of residual blocks, the number of repetitions of the separation blocks and the number of channels in the depth-wise convolutions, and experimentally evaluate how they affect the speech separation performance. In particular, experiments carried out on the Libri2Mix show that the number of dilated 1D-Conv blocks is the most critical parameter and that the usage of extra-dilation in the residual blocks allows reducing the performance drop.

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