Speaker Counting Based on a Novel Hive Shaped Nested Microphone Array by WPT and 2D Adaptive SRP Algorithms in Near-Field Scenarios

Speech processing algorithms, especially sound source localization (SSL), speech enhancement, and speaker tracking are considered to be the main fields in this application. Most speech processing algorithms require knowing the number of speakers for real implementation. In this article, a novel method for estimating the number of speakers is proposed based on the hive shaped nested microphone array (HNMA) by wavelet packet transform (WPT) and 2D sub-band adaptive steered response power (SB-2DASRP) with phase transform (PHAT) and maximum likelihood (ML) filters, and, finally, the agglomerative classification and elbow criteria for obtaining the number of speakers in near-field scenarios. The proposed HNMA is presented for aliasing and imaging elimination and preparing the proper signals for the speaker counting method. In the following, the Blackman–Tukey spectral estimation method is selected for detecting the proper frequency components of the recorded signal. The WPT is considered for smart sub-band processing by focusing on the frequency bins of the speech signal. In addition, the SRP method is implemented in 2D format and adaptively by ML and PHAT filters on the sub-band signals. The SB-2DASRP peak positions are extracted on various time frames based on the standard deviation (SD) criteria, and the final number of speakers is estimated by unsupervised agglomerative clustering and elbow criteria. The proposed HNMA-SB-2DASRP method is compared with the frequency-domain magnitude squared coherence (FD-MSC), i-vector probabilistic linear discriminant analysis (i-vector PLDA), ambisonics features of the correlational recurrent neural network (AF-CRNN), and speaker counting by density-based classification and clustering decision (SC-DCCD) algorithms on noisy and reverberant environments, which represents the superiority of the proposed method for real implementation.