Image Processing Techniques for Segments Grouping in Monaural Speech Separation

Abstract

Monaural speech separation is the process of separating the target speech from the noisy speech mixture recorded using single microphone. It is a challenging problem in speech signal processing, and recently, computational auditory scene analysis (CASA) finds a reasonable solution to solve this problem. This research work proposes an image analysis-based algorithm to enhance the binary T–F mask obtained in the initial segmentation stage of CASA-based monaural speech separation systems to improve the speech quality. The proposed algorithm consists of labeling the initial segmentation mask, boundary extraction, active pixel detection and finally eliminating the noisy non-active pixels. In labeling, the T–F mask obtained from the initial segmentation is labeled as periodicity pixel matrix and non-periodicity pixel matrix. Next boundaries are created by connecting all the possible nearby periodicity pixel matrix and non-periodicity pixel matrix as speech boundary. Some speech boundary may include noisy T–F units as holes, and these holes are treated using the proposed algorithm to properly classify them as the speech-dominant or noise-dominant T–F units in the active pixel detection process. Finally, the noisy T–F units are eliminated. The performance of the proposed algorithm is evaluated using TIMIT speech database. The experimental results show that the proposed algorithm improves the quality of the separated speech by increasing the signal-to-noise ratio by an average value of 9.64 dB and reduces the noise residue by 25.55% as compared to the noisy speech mixture.