Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders
This addresses the challenge of feature dependency in audio source separation for music processing, though it appears incremental as it builds on existing auto-encoder methods.
The authors tackled the problem of multi-channel audio source separation by introducing a multi-resolution convolutional auto-encoder that operates on raw time-domain signals, achieving separation of singing-voice from stereo music without hand-crafted features or pre/post-processing.
Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is therefore largely dependent on the choice of features used for training. In this work, we introduce a novel multi-channel, multi-resolution convolutional auto-encoder neural network that works on raw time-domain signals to determine appropriate multi-resolution features for separating the singing-voice from stereo music. Our experimental results show that the proposed method can achieve multi-channel audio source separation without the need for hand-crafted features or any pre- or post-processing.