Section: New Software and Platforms

dnnsep

Multichannel audio source separation with deep neural networks

Keywords: Audio - Source Separation - Deep learning

Scientific Description: dnnsep is the only source separation software relying on multichannel Wiener filtering based on deep learning. Deep neural networks are used to initialize and reestimate the power spectrum of the sources at every iteration of an expectation-maximization (EM) algorithm. This results in state-of-the-art separation quality for both speech and music.

Functional Description: Combines deep neural networks and multichannel signal processing for speech enhancement and separation of musical recordings.

Release Functional Description: This version derives from version 1.0 (not 1.9). Differences concerns the use of a bidirectional long short-term memory (BLSTM) neural network, smoothing of the multichannel Wiener filter (MWF) over time and frequency, usage of the principal component of the MWF filter, adding a new generalized eigenvector beamformer with blind analytical normalization (GEVB) filter, and normalizing the training and test signals.

• Participants: Aditya Nugraha, Emmanuel Vincent and Antoine Liutkus

• Contact: Emmanuel Vincent