Abstract
In reverberant and noisy environments, tracking a speech source in distributed microphone networks is a challenging problem. A speech source tracking method based on distributed particle filter (DPF) and average consensus algorithm (ACA) is proposed in distributed microphone networks. The generalized cross-correlation (GCC) function is used to approximate the time difference of arrival (TDOA) of speech signals received by two microphones at each node. Next, the multiple-hypothesis model based on multiple TDOAs is calculated as the local likelihood function of the DPF. Finally, the ACA is applied to fuse local state estimates from local particle filter (PF) to obtain a global consensus estimate of the speech source at each node. The proposed method can accurately track moving speech source in reverberant and noisy environments with distributed microphone networks, and it is robust against the node failures. Simulation results reveal the validity of the proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Spexard, T.P., Hanheide, M., Sagerer, G.: Human-oriented interaction with an anthropomorphic robot. IEEE Trans. Robotics 23(5), 852–862 (2007)
Chen, B.W., Chen, C.Y., Wang, J.F.: Smart homecare surveillance system: behavior identification based on state-transition support vector machines and sound directivity pattern analysis. IEEE Trans. Syst., Man, Cybern. A Syst. 43(6), 1279–1289 (2013)
Kapralos, B., Jenkin, M.R.M., Evangelos, M.: Audiovisual localization of multiple speakers in a video teleconferencing setting. Int. J. Imaging Syst. Technol. 13(1), 95–105 (2003)
Nakadai, K., Nakajima, H., Murase, M., et al.: Robust tracking of multiple sound sources by spatial integration of room and robot microphone arrays. In: International Conference on Acoustic, Speech, Signal Process, Toulouse, France, pp. IV-929–IV-932 (2006)
Ward, D.B., Lehmann, E.A., Williamson, R.C.: Particle filtering algorithms for tracking an acoustic source in a reverberant environment. IEEE Trans. Speech Audio Process. 11(6), 826–836 (2003)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)
Talantzis, F.: An acoustic source localization and tracking framework using particle filtering and information theory. IEEE Trans. Audio Speech Lang. Process. 18(7), 1806–1817 (2010)
Zhong, X., Hopgood, J.R.: A time-frequency masking based random finite set particle filtering method for multiple acoustic source detection and tracking. IEEE Trans. Audio Speech Lang. Process. 23(12), 2356–2370 (2015)
Zhong, X., Hopgood, J.R.: Particle filtering for TDOA based acoustic source tracking: nonconcurrent multiple talkers. Signal Process. 96(5), 382–394 (2014)
Zhong, X., Mohammadi, A., Wang, W., Premkumar, A.B., Asif, A.: Acoustic source tracking in a reverberant environment using a pairwise synchronous microphone network. In: 16th International Conference on Information Fusion, Istanbul, Turkey, pp. 953–960 (2013)
Zhang, Q., Chen, Z., Yin, F.: Speaker tracking based on distributed particle filter in distributed microphone networks. IEEE Trans. Syst. Man Cybern. Syst. 47(9), 2433–2443 (2017)
Wang, R., Chen, Z., Yin, F., Zhang, Q.: Distributed particle filter based speaker tracking in distributed microphone networks under non-Gaussian noise environments. Digital Signal Process. 63, 112–122 (2017)
Hlinka, O., Hlawatsch, F., Djuric, P.M.: Distributed particle filtering in agent networks: a survey, classification, and comparison. IEEE Signal Process. Mag. 30(1), 61–81 (2013)
Lin, X. and Boyd, S.: Fast linear iterations for distributed averaging. In: 42nd International Conference on Decision and Control, Maui, USA, pp. 4997–5002 (2004)
Knapp, C., Carter, G.C.: The generalized correlation method for estimation of time delay. IEEE Trans. Acoust. Speech Signal Process. 24(4), 320–327 (1976)
Mohammadi, A., Asif, A.: Consensus-based distributed dynamic sensor selection in decentralized sensor networks using the posterior Cramér-Rao lower bound. Signal Process. 108, 558–575 (2015)
Lehmann, E.A., Johansson, A.M. and Nordholm, S.: Reverberation-time prediction method for room impulse responses simulated with the image-source model. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, USA, pp. 159–162 (2007)
Acknowledgement
This work was supported by National Science Foundation for Young Scientists of China (Grant No.61801308).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, R., Lan, X. (2019). Speech Source Tracking Based on Distributed Particle Filter in Reverberant Environments. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-030-36405-2_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-36405-2_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36404-5
Online ISBN: 978-3-030-36405-2
eBook Packages: Computer ScienceComputer Science (R0)