Wide-band dereverberation method based on multichannel linear prediction using prewhitening filter

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Abstract Several dereverberation algorithms have been studied. The sampling frequencies used in conventional studies are typically 8–16 kHz because their main purpose is preprocessing for improving the intelligibility of speech communication and articulation for automatic speech recognition. However, in next-generation communication systems, techniques to analyze and reproduce not only semantic information of sound but also more high-definition components such as spatial information and directivity will be increasingly necessary. To decompose these sound field characteristics with high definition, a dereverberation algorithm that is useful at high sampling frequencies is an important technique to process sound that includes high-frequency spectra such as musical sounds. The LInear-predictive Multichannel Equalization (LIME) algorithm is a promising dereverberation method. Using the LIME algorithm, however, a dereverberation signal cannot be solved at high sampling frequencies when the source signal is colored, such as in the case of speech and sound of musical signals. Because the rank of the correlation matrix calculated from such a colored signal is not full, the characteristic polynomial cannot be calculated precisely. To alleviate this problem, we propose preprocessing of all input signals with filters to whiten their spectra so that this algorithm can function for colored signals at high sampling frequencies.