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Multiple-Speech-Source DOA Estimation Based on Single-Source Cluster Detection
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DOI：10.1109/TASLP.2023.3321213.png)
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Abstract
En 中文
This study proposes multiple-speech-source direction -of-arrival (DOA) estimation based on the distribution characteristic of the time-frequency (TF) point dominated by a single-source component (i.e., single-source point, SSP). By exploring the TF distribution characteristics of SSPs, we found that most are distributed in clusters in the TF domain. Hence, the concept of a single-source cluster (SSC) is given, each composed of adjacent TF points from one dominant sound source. Considering that SSCs have different shapes and sizes, an SSC detection method is designed based on point-to-cluster expansion, which is the research focus of this article. A two-dimensional Gaussian function is introduced to model the theoretical distribution of the DOAs of SSPs, and a cluster expansion rule is proposed based on hypothesis testing of the DOA of a source. Two-dimensional kernel density estimation and peak search are adopted to estimate the DOAs and the number of sources using the detected SSCs. Experimental results in both simulated and real environments show that the proposed method can achieve better DOA estimation performance than some current techniques.
Keywords:
Direction-of-arrival estimation
Estimation
Location awareness
Microphone arrays
Reverberation
Reflection
Recording
DOA estimation
single-source cluster detection
hypothesis testing
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Citations:
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