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We present MarbleNet, an end-to-end neural network for Voice ActivityDetection (VAD). MarbleNet is a deep residual network composed from blocks of1D time-channel separable convolution, batch-normalization, ReLU and dropoutlayers. When compared to a state-of-the-art VAD model, MarbleNet is able toachieve similar performance with roughly 1/10-th the parameter cost. We furtherconduct extensive ablation studies on different training methods and choices ofparameters in order to study the robustness of MarbleNet in real-world VADtasks.

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