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

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arxiv 2010.13886 v2 pith:YZ7RCBKN submitted 2020-10-26 eess.AS cs.SD

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

Fei Jia , Somshubra Majumdar , Boris Ginsburg This is my paper
classification eess.AS cs.SD
keywords marblenetnetworkactivitydeepdetectionneuralseparabletime-channel
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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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  1. VAD to the Bone: Ultra-Tiny Speech Activity Detection for Edge Deployment

    eess.AS 2026-07 conditional novelty 6.0

    kiloVAD, a 2.1k-parameter causal CNN VAD on standard Mel features, reaches 0.850 AUC on AVA-Speech and beats standard QAT by 1–4% at INT4 via angle-based self-distillation.