Online Speech Dereverberation Algorithm Based on Adaptive Multichannel Linear Prediction

This paper proposes a real-time acoustic channel equalization method that uses an adaptive multichannel linear prediction technique. In general, multichannel equalization algorithms can eliminate reverberation if they meet the following specific conditions including: the co-primeness between channels and sufficient filter length. It also requires the characteristic of correct channel information, however, it is difficult to estimate accurate acoustic channels in a practical system. The proposed method utilizes a theoretically perfect channel equalization algorithm and considers problems that may arise in the actual system. Linear-predictive multi-input equalization (LIME) is also an appropriate attempt at blind dereverberation by assuring the theoretical basis. However, a huge computational cost is incurred by calculating the large dimensions of a covariance matrix and its inversion. The proposed equalizer is developed as a multichannel linear prediction (MLP) oriented structure with a new formula that is optimized to time-varying acoustical room environments. Moreover, experimental results show that the proposed method works well even if the channel characteristics of each microphone are similar. The results of experiments using various room impulse response (RIR) models, including both the synthesized and real room environments, show that the proposed method is superior to conventional methods.