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

Fei Jia; Somshubra Majumdar; Boris Ginsburg

ICASSP 2021accepted0 citations

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

Fei Jia, Somshubra Majumdar, Boris Ginsburg

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.

BibTeX

@inproceedings{icassp2021_marblenetdeep1dt,
  title = {MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection},
  author = {Fei Jia and Somshubra Majumdar and Boris Ginsburg},
  booktitle = {ICASSP 2021},
  year = {2021}
}