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A number of applications in acoustics, such as echo cancellation, require learning the acoustic impulse response from each deployed loudspeaker to each microphone-the room transfer function. This has conventionally been done separately at each microphone for each loudspeaker. However, the signals arriving at the array share a common structure, which can be exploited to improve the impulse response estimates. In this work, we propose an algorithm that takes advantage of the array structure, asdoi:10.1109/lsp.2013.2297832 fatcat:xj2rp3i4erclxcprujxwrqu5ve
more »... ll as the sparsity of the reflections arriving at the array in order to form reliable estimates of the impulse response between each loudspeaker and microphone. The algorithm is shown to improve performance over the matched filter algorithm in echo cancellation applications, using both synthetic and real data.
The solution of inverse problems where the parameter being estimated has a known structure has been widely studied. In this work, we consider the situation where it is not appropriate to assume a structure for the parameter, but the observations on which the estimate are based are structured; specifically, when the observations are parametrized by a decomposable graphical model. This translates to structured sparsity of the inverse covariance matrix for Gaussian distributed observation vectors.doi:10.1109/icassp.2014.6854689 dblp:conf/icassp/YellepeddiP14 fatcat:shdkhygovfbytcjtkutk6twaqm
more »... We present an approximate least squares method which takes advantage of the structure to reduce the complexity of least squares. The approximate least squares method can be implemented recursively for even lower complexity. It is shown that the proposed method is asymptotically equivalent to least squares parameter estimation for a large number of observations. The properties of the algorithm are verified by simulation.
Yellepeddi was with Mitsubishi Electric Research Laboratories during this work. ...doi:10.1109/icc.2013.6655449 dblp:conf/icc/YellepeddiKDO13 fatcat:sw63kwze45hupe3fkrhof6idze
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Ltd, China IVMSP-P11.3: TRACKING TO IMPROVE DETECTION QUALITY .... 2683 AUTONOMOUS DRIVING Jennifer Tang, Massachusetts Institute of Technology, United States; Atulya Yellepeddi, Sefa Demirtas, Christopher ...doi:10.1109/icassp40776.2020.9054406 fatcat:6h7hh2hxhne4pbmphharu2et2m