Signal Restoration based on Bi-directional LSTM with Spectral Filtering for Robot Audition

Abstract

This paper addresses restoration of acoustic signals for robot audition. A robot usually listens to target acoustic signals such as speech and music in noisy conditions. Acoustic information on such signals inevitably contaminated with noise. Even when noise reduction techniques such as sound source separation are performed, the noise-reduced acoustic signals contain distortion and/or residual noise after the noise reduction to some extent. The distortion and residual noise basically degrade the performance of recognition processes such as automatic speech recognition (ASR). We decided to use bidirectional long short-term memory (Bi-LSTM) for acoustic signal restoration since it can represent dynamic behaviors well for a temporal sequence in the forward and backward directions. When applying Bi-LSTM to recover acoustic signals, there is an issue, that is, acoustic signals tend to be sparse in high frequencies, and thus Bi-LSTM training becomes insufficient in such high frequencies due to a lack of training data. Therefore, we propose a new restoration method based on Bi-LSTM with spectral filtering. The spectral filter and the corresponding inverse filter are introduced to a Bi-LSTM framework to accelerate training in high frequencies. Preliminary results showed that the proposed Bi-LSTM with spectral filtering can perform signal restoration even when a small amount of training data is available.