TIME-FREQUENCY MASKING BASED ONLINE SPEECH ENHANCEMENT WITH MULTI-CHANNEL DATA USING CONVOLUTIONAL NEURAL NETWORKS

Speech enhancement in noisy and reverberant conditions remains a challenging task. In this work, a time-frequency masking based method for speech enhancement with multi-channel data using convolutional neural networks (CNN) is proposed, where the CNN is trained to estimate the ideal ratio mask by discriminating directional speech source from diffuse or spatially uncorrelated noise. The proposed method operates on, frame-by-frame, the magnitude and phase components of the short-time Fourier transform coefficients of all frequency sub-bands and microphones. The avoidance of temporal context and explicit feature extraction makes the proposed method suitable for online implementation. In contrast to most speech enhancement methods that utilize multi-channel data, the proposed method does not require information about the spatial position of the desired speech source. Through experimental evaluation with both simulated and real data, we show the robustness of the proposed method to unseen acoustic conditions as well as varying noise levels.