MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection
This work addresses voice activity detection for audio processing applications, but it is incremental as it builds on existing neural network architectures with efficiency improvements.
The authors tackled the problem of voice activity detection by introducing MarbleNet, a deep residual network using 1D time-channel separable convolutions, which achieved similar performance to a state-of-the-art model with about 1/10th the parameter cost.
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.