MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection

Fei Jia; Somshubra Majumdar; Boris Ginsburg

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arXiv · 2010.13886

MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection

Fei Jia·Somshubra Majumdar·Boris Ginsburg

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Abstract

We present MarbleNet, an end-to-end neural network for Voice Activity Detection (VAD). MarbleNet is a deep residual network composed from blocks of 1D time-channel separable convolution, batch-normalization, ReLU and dropout layers. When compared to a state-of-the-art VAD model, MarbleNet is able to achieve similar performance with roughly 1/10-th the parameter cost. We further conduct extensive ablation studies on different training methods and choices of parameters in order to study the robustness of MarbleNet in real-world VAD tasks.

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Fei Jia, Somshubra Majumdar, Boris Ginsburg. 2020-10-26. MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection. https://arxiv.org/abs/2010.13886

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