Journal Article10.1007/S10772-016-9341-9
Performance of speaker localization using microphone array
4
TL;DR: A new speaker localization algorithm known as group search optimization (GSO) algorithm is proposed and experimental results show that the proposed GSO method outperforms the other methods in terms of mean square error, root mean squareerror, meanabsolute error, mean absolute percentage error, euclidean distance and mean absolute relative error.
read more
Abstract: Speaker localization is a technique to locate and track an active speaker from multiple acoustic sources using microphone array. Microphone array is used to improve the speech quality of recorded speech signal in meeting room and other places. In this work, the time delay estimation between source and each microphone is calculated using a localization method called time differences of arrival (TDOA). TDOA localization consists of two steps namely (a) a time delay estimator and (b) a localization estimator. For time delay estimation, the generalized cross-correlation using phase transform, the generalized cross correlation using maximum likelihood, linear prediction (LP) residual and the Hilbert envelope of the LP residual are chosen for estimating the location of a person. A new speaker localization algorithm known as group search optimization (GSO) algorithm is proposed. The performance of this algorithm is analyzed and compared with Gauss–Newton nonlinear least square method and genetic algorithm. Experimental results show that the proposed GSO method outperforms the other methods in terms of mean square error, root mean square error, mean absolute error, mean absolute percentage error, euclidean distance and mean absolute relative error.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Walking direction detection using received signal strengths in correlated RF links
Tong Liu,Zhi-ming Chen,Zhuo-qian Liang +2 more
- 01 Dec 2017
TL;DR: This paper uses two correlated links based on the most existing network to sense the walking movement and extracts the short-time variances of received signal strength (RSS) on both links as the motion feature to identify the direction of walking motion.
3
Robust Time-Difference-of-Arrival (TDOA) Localization Using Weighted Least Squares with Cone Tangent Plane Constraint.
Bonan Jin,Xiaosu Xu,Tao Zhang +2 more
TL;DR: A weighted-least-squares (WLS) algorithm with the cone tangent plane constraint for hyperbolic positioning and shows that this algorithm is accurate and robust under poor external environment.
Topology Optimization of Planar Microphone Array Based on NSGA-II
Yanling Lv,Kexian Ai +1 more
TL;DR: This study optimizes planar microphone array topology using NSGA-II to remove redundant elements, improving sidelobe level and detection accuracy by 46%, and enhancing positioning performance through beam-forming algorithm and experimental verification.
Speaker Localization in Smartphones Using Adaptive Eigenvalue Decomposition with Noise Reduction
Jose Marie Mendoza,Franz de Leon +1 more
TL;DR: This paper proposes an adaptive eigenvalue decomposition (AED) algorithm with noise reduction for speaker localization in smartphones, achieving 69.87% accuracy and improving to 79.28% with tilt compensation, outperforming previous SSL algorithms in smartphones.
References
The generalized correlation method for estimation of time delay
TL;DR: In this paper, a maximum likelihood estimator is developed for determining time delay between signals received at two spatially separated sensors in the presence of uncorrelated noise, where the role of the prefilters is to accentuate the signal passed to the correlator at frequencies for which the signal-to-noise (S/N) ratio is highest and suppress the noise power.
4.8K
Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior
TL;DR: A novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior, and has competitive performance to other EAs in terms of accuracy and convergence speed, especially on high-dimensional multimodal problems.
728
An overview on the time delay estimate in active and passive systems for target localization
TL;DR: The analysis shows that in the case of low SNR and when signal and noise autospectra are constants over the band or signal and noises fall off at the same rate, the minimum standard deviation of the time delay estimate varies inversely to the SNR, to the square root of the product of observation time and bandwidth, and to the center frequency.
424
Voice source localization for automatic camera pointing system in videoconferencing
Hong Wang,P.L. Chu +1 more
- 21 Apr 1997
TL;DR: This paper describes the voice source localization algorithm used in the PictureTel automatic camera pointing system (LimeLight/sup TM/, dynamic speech locating technology), which uses an array of 46 cm wide and 30 cm high, which contains 4 microphones, and is mounted on top of the monitor.