Exploiting Long-Term Temporal Dependencies in NMF using Recurrent Neural Networks with Application to Source Separation

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

Publication date: May 9, 2014

Nicolas Boulanger-Lewandowski, Gautham Mysore, Matthew Hoffman

This paper seeks to exploit high-level temporal information during feature extraction from audio signals via non-negative matrix factorization. Contrary to existing approaches that impose local temporal constraints, we train powerful recurrent neural network models to capture long-term temporal dependencies and event co-occurrence in the data. This gives our method the ability to “fill in the blanks” in a smart way during feature extraction from complex audio mixtures, an ability very useful for a number of audio applications. We apply these ideas to source separation problems.

Research Areas:  Adobe Research iconAI & Machine Learning Adobe Research iconAudio