Abstract
Multiple sound sources localization is a hot topic in audio signal processing and is widely utilized in many application areas. This paper proposed a multiple sound sources localization method based on a statistically dominant source component removal (SDSCR) algorithm by soundfield microphone. The existence of the statistically weak source (SWS) among soundfield microphone signals is validated by statistical analysis. The SDSCR algorithm with joint an intra-frame and inter-frame statistically dominant source (SDS) discriminations is designed to remove the component of SDS while reserve the SWS component. The degradation of localization accuracy caused by the existence of the SWS is resolved using the SDSCR algorithm. The objective evaluation of the proposed method is conducted in simulated and real environments. The results show that the proposed method achieves a better performance compared with the conventional SSZ-based method both in sources localization and counting.
Highlights
Multiple sound sources localization is an important topic in audio signal processing and has received a lot of attention in over recent decades [1]
In order to find out the statistically weak source (SWS) to improve localization performance, we present a multiple sound sources localization method based on statistically dominant source component removal (SDSCR) algorithm in this paper
single source zones (SSZ) are associated with informal experiment, we find that if the number of SSZs are associated with one sound source less one sound source less than 4% of the total number of SSZs, the peak corresponding to these sources than 4% of the total number of SSZs, the peak corresponding to these sources cannot be detected in cannot be detected in the direction of arrival (DOA) estimation histogram
Summary
Multiple sound sources localization is an important topic in audio signal processing and has received a lot of attention in over recent decades [1]. When the number of simultaneously occurring sources are four or above, more than one source is active in a T-F bin with a high probability It means that this assumption is less accurate when the number of sound sources increases, which would affect the localization accuracy of the SCA-based method. To address this issue, a localization method that applied the relaxed sparsity constraints of multiple sound sources has been proposed in Reference [27]. It means that as the number of simultaneously occurring sound sources increase, excessive microphones are required to ensure a reliable accuracy To solve this problem, an improvement of this DOA estimation method has been proposed [28] to get a high accuracy of multiple sound sources localization by soundfield microphone. After the localization performance evaluation in simulated and real environments, the proposed method shows good performance both in DOA estimation and sources counting
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