In this work, a multiple sound source localization and counting method based on a relaxed sparsity of speech signal is presented. A soundfield microphone is adopted to overcome the redundancy and complexity of microphone array in this paper. After establishing an effective measure, the relaxed sparsity of speech signals is investigated. According to this relaxed sparsity, we can obtain an extensive assumption that ''single-source'' zones always exist among the soundfield microphone signals, which is validated by statistical analysis. Based on ''singlesource'' zone detecting, the proposed method jointly estimates the number of active sources and their corresponding DOAs by applying a peak searching approach to the normalized histogram of estimated DOA. The cross distortions caused by multiple simultaneously occurring sources are solved by estimating DOA in these ''single-source'' zones. The evaluations reveal that the proposed method achieves a higher accuracy of DOA estimation and source counting compared with the existing techniques. Furthermore, the proposed method has higher efficiency and lower complexity, which makes it suitable for real-time applications.

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A single source bins (SSBs) detection based multiple source localisation scheme is proposed. This scheme is based on detecting the SSBs in mixture signals that are only derived from one source. Specifically, after proposing a 'DOA convergence' assumption, K-means clustering algorithm is used for SSBs detecting. Thus, the multiple source localisation is converted to a single source one among these SSBs. Moreover, the proposed SSBs detection is applicable to other localisation methods and not limited to specific microphone topology. Experimental results demonstrate the localisation accuracy of the proposed method outperforms the localisation approaches which are based on single source zone detection.

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