For Time Difference Of Arrival (TDOA) estimation of multiple sound sources with wide spacing under indoor environment, an unambiguous algorithm based on approximated Kernel Destiny Estimator (KDE) is studied. According to the short-time spectral sparseness of audio signals, the time-frequency bin with energy dominance of a single source is extracted from Coherence Test (CT), then an approximated kernel function constructed of Normalized Cross-Spectrum (NCS) of obtained signals is used to weaken the interference of indoor reverberation with cumulative average, while adding Multi-Stage (MS) to divide the frequency band, the spatial ambiguity with wide spacing can be solved effectively. This algorithm is verified as an unambiguous TDOA estimation algorithm of multi-source under indoor environment by both theoretical derivation and simulation results.
KNAPP C H and CARTER G C. The generalized correlation method for estimation of time delay[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1976, 24(4): 320-327.
[2]
TSIAMI A, KATSAMANIS A, MARAGOS P, et al. Experiments in acoustic source localization using sparse arrays in adverse indoors environments[C]. Proceedings of 2014 European Signal Processing Conference (EUSIPCO), Lisbon, Portugal, 2014: 2390-2394.
ZHANG Chao, WU Xiaopei, and LÜ Zhao. Experiments and analysis on observation vector generation and channel number selection in motion detection algorithm based on independent component analysis[J]. Journal of Electronics & Information Technology, 2015, 37(1): 137-142. doi: 10.11999/ JEIT140197.
[4]
LOMBARD A, ZHENG Y, BUCHNER H, et al. TDOA estimation for multiple sound sources in noisy and reverberant environments using broadband independent component analysis[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2011, 19(6): 1490-1503.
[5]
NESTA F, SVAIZER P, and OMOLOGO M. Cumulative state coherence transform for a robust two-channel multiple source localization[C]. Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation (ICA), Berlin, Germany, 2009: 290-297.
[6]
NESTA F and OMOLOGO M. Generalized state coherence transform for multidimensional TDOA estimation of multiple sources[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(1): 246-260.
[7]
REDDY V V, KHONG W H, and NG B P. Unambiguous speech DOA estimation under spatial aliasing conditions[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2014, 22(12): 2133-2145.
[8]
YILMAZ O and RICKARD S. Blind separation of speech mixtures via time-frequency masking[J]. IEEE Transactions on Signal Processing, 2004, 52(7): 1830-1847.
[9]
ARAKI S, SAWADA H, MUKAI R, et al. DOA estimation for multiple sparse sources with normalized observation vector clustering[C]. Proceedings of 2006 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP 2006), Toulouse, France, 2006: 33-36.
[10]
BRUTTI A and NESTA F. Tracking of multidimensional TDOA for multiple sources with distributed microphone pairs[J]. Computer Speech & Language, 2013, 27(3): 660-682.
[11]
THO N T N, ZHAO Shengkui, and JONES D L. Robust DOA estimation of multiple speech sources[C]. Proceedings of 2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Florence, Italy, 2014: 2287-2291.
XU Zhiyong, ZHAO Zhao, and LIU Ming. Real-time unambiguious passive direction finding for multiple sound sources with widely spaced microphone array[J]. Journal of Electronics & Information Technology, 2011, 33(9): 2056-2061. doi: 10.3724/SP.J.1146.2010.01273.
[13]
GUSTAFFSON T, RAO B D, and TRIVEDI M. Source localization in reverberant environments: Modeling and statistical analysis[J]. IEEE Transactions on Speech and Audio Processing, 2003, 11(6): 791-803.
[14]
LEHMANN E and JOHANSSON A. Prediction of energy decay in room impulse responses simulated with an image-source model[J]. Acoustical Society of America, 2008, 124(1): 269-277.
[15]
BLANDIN C, OZEROV A, and VINCENT E. Multi-source TDOA estimation in reverberant audio using angular spectra and clustering[J]. Signal Processing, 2012, 92(8): 1950-1960.
