Spatial consistency for multiple source direction-of-arrival estimation and source counting

Abstract

A conventional approach to wideband Multi-Source (MS) Direction-of-Arrival (DOA) estimation is to perform Single Source (SS) DOA estimation in Time-Frequency (TF) bins for which a SS assumption is valid. The typical SS-validity confidence metrics analyse the validity of the SS assumption over a fixed-size TF region local to the TF bin. The performance of such methods degrades as the number of simultaneously active sources increases due to the associated decrease in the size of the TF regions where the SS assumption is valid. A SS-validity confidence metric is proposed that exploits a dynamic MS assumption over relatively larger TF regions. The proposed metric first clusters the initial DOA estimates (one per TF bin) and then uses the members' spatial consistency as well as its cluster's spread to weight each TF bin. Distance-based and density-based clustering are employed as two alternative approaches for clustering DOAs. A noise-robust density-based clustering is also used in an evolutionary framework to propose a method for source counting and source direction estimation. The evaluation results based on simulations and also with real recordings show that the proposed weighting strategy significantly improves the accuracy of source counting and MS DOA estimation compared to the state-of-the-art.

Cell-Based Biohybrid Sensor Device for Chemical Source Direction Estimation

“…Such an approach requires an array of sensors and detectors to obtain data, which results in complicated detection systems. Many studies have used a similar strategy to estimate the source direction of sounds and sonar signals [ 22 , 23 ]; however, few studies have focused on such strategies for arrayed cell-based biohybrid sensor systems. Our approach involving the use of microstructures to restrain the chemical-solution-flow direction to the array of cells necessitates the use of only one fluorescence imaging system to estimate the chemical-source direction based on the difference in the analyte arrival time to each ROI.…”

Multisource localization based on angle distribution of time–frequency points using an FOA microphone

“…In addition, an SSP detection method is designed based on the direct‐path dominance test [27]. Following a single‐source validity confidence metric, an SSP‐based DOA estimation method was designed by applying K‐means clustering and density‐based spatial clustering of applications with noise [28]. However, while these methods can obtain high accuracy of DOA estimates, they rely on a large number of microphones.…”