TY - GEN
T1 - Source and Direction of Arrival Estimation Based on Maximum Likelihood Combined with GMM and Eigenanalysis
AU - Nishimura, R.
AU - Suzuki, Y.
N1 - Funding Information:
∗This research and development work was supported by the MIC/SCOPE #171502001.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - A method is proposed for estimating the source signal and its direction of arrival (DOA) in this paper. It is based on ML estimation of the transfer function between microphones combined with the EM algorithm for a Gaussian Mixture Model (GMM), assuming that the signal is captured at each microphone with delay corresponding to the traveling of sound and some decay. By this modeling, search for the maximum log-likelihood in the ML estimation can be realized simply by eigenvalue decomposition of a properly designed matrix. Computer simulation results show that the proposed method achieves SDR of greater than 10 dB regardless of amplitude difference between microphones and DOA estimation error of less than 8 degrees, on average. It is also shown that it can maintain high performance in various conditions.
AB - A method is proposed for estimating the source signal and its direction of arrival (DOA) in this paper. It is based on ML estimation of the transfer function between microphones combined with the EM algorithm for a Gaussian Mixture Model (GMM), assuming that the signal is captured at each microphone with delay corresponding to the traveling of sound and some decay. By this modeling, search for the maximum log-likelihood in the ML estimation can be realized simply by eigenvalue decomposition of a properly designed matrix. Computer simulation results show that the proposed method achieves SDR of greater than 10 dB regardless of amplitude difference between microphones and DOA estimation error of less than 8 degrees, on average. It is also shown that it can maintain high performance in various conditions.
KW - Gaussian Mixture Model
KW - ML estimation
KW - Rayleigh quotient
KW - Sparseness
KW - Time-frequency masking
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U2 - 10.1109/ICASSP.2018.8461658
DO - 10.1109/ICASSP.2018.8461658
M3 - Conference contribution
AN - SCOPUS:85054209554
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3434
EP - 3438
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -