Modeling high-dimensional sequences with recurrent neural networks

February 28, 2014
Nicolas Maurice Boulanger-Lewandowski | University of Montréal

Humans commonly understand sequential events by giving importance to what they expect rather than exclusively to what they actually observe. The ability to fill in the blanks, useful in speech recognition to favor words that make sense in the current context, is particularly important in noisy conditions. In this talk, we present a probabilistic model of symbolic sequences based on a recurrent neural network that can serve as a powerful prior during information retrieval. We show that conditional distribution estimators can describe much more realistic output distributions, and we devise inference procedures to efficiently search for the most plausible annotations when the observations are partially destroyed or distorted. We demonstrate improvements in the state of the art in polyphonic music transcription, chord recognition, speech recognition and audio source separation.

Speaker Details

Nicolas Boulanger-Lewandowski is a PhD student at University of Montréal, working in the LISA machine learning laboratory with Profs. Yoshua Bengio and Pascal Vincent. His research focuses on machine learning, neural networks, sequential models and music information retrieval.