RESEARCH

SPEECH & AUDIO

Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remix

May 15, 2019

Abstract

Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of generative adversarial models presented new opportunities in signal regression tasks. The power of adversarial training however has not yet been realized for blind source separation tasks. In this work, we propose a novel method for blind source separation (BSS) using adversarial methods. We rely on the independence of sources for creating adversarial constraints on pairs of approximately separated sources, which ensure good separation. Experiments are carried out on image sources validating the good performance of our approach, and presenting our method as a promising approach for solving BSS for general signals.

Download the Paper

Related Publications

May 15, 2019

SPEECH & AUDIO

Towards Unsupervised Single-Channel Blind Source Separation using Adversarial Pair Unmix-and-Remix | Facebook AI Research

Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of…

Yedid Hoshen

May 15, 2019

June 02, 2019

SPEECH & AUDIO

NLP

The emergence of number and syntax units in LSTM language models | Facebook AI Research

Recent work has shown that LSTMs trained on a generic language modeling objective capture syntax-sensitive generalizations such as long-distance number agreement. We have however no mechanistic understanding of how they accomplish this…

Yair Lakretz, Germán Kruszewski, Theo Desbordes, Dieuwke Hupkes, Stanislas Dehaene, Marco Baroni

June 02, 2019

June 01, 2019

SPEECH & AUDIO

NLP

Neural Models of Text Normalization for Speech Applications | Facebook AI Research

Machine learning, including neural network techniques, have been applied to virtually every domain in natural language processing. One problem that has been somewhat resistant to effective machine learning solutions is text normalization for…

Hao Zhang, Richard Sproat, Axel H. Ng, Felix Stahlberg, Xiaochang Peng, Kyle Gorman, Brian Roark

June 01, 2019

May 17, 2019

COMPUTER VISION

SPEECH & AUDIO

GLoMo: Unsupervised Learning of Transferable Relational Graphs | Facebook AI Research

Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However,…

Zhilin Yang, Jake (Junbo) Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun

May 17, 2019

Related Work

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.