@conference {13953,
	title = {Automatic matched filter recovery via the audio camera},
	booktitle = {Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on},
	year = {2010},
	month = {2010/03//},
	pages = {2826 - 2829},
	abstract = {The sound reaching the acoustic sensor in a realistic environment contains not only the part arriving directly from the sound source but also a number of environmental reflections. The effect of those on the sound is equivalent to a convolution with the room impulse response and can be undone via deconvolution - a technique known as matched filter processing. However, the filter is usually pre-computed in advance using known room geometry and source/receiver positions, and any deviations from those cause the performance to degrade significantly. In this work, an algorithm is proposed to compute the matched filter automatically using an audio camera - a microphone array based system that provides real-time audio images (essentially plots of steered response power in various directions) of environment. Acoustic sources, as well as their significant reflections, are revealed as peaks in the audio image. The reflections are associated with sound source(s) using an acoustic similarity metric, and an approximate matched filter is computed to align the reflections in time with the direct arrival. Preliminary experimental evaluation of the method is performed. It is shown that in case of two sources the reflections are identified correctly, the time delays recovered agree well with those computed from geometric constraints, and that the output SNR improves when the reflections are added coherently to the signal obtained by beamforming directly at the source.},
	keywords = {acoustic, array;real-time, arrays;transient, audio, camera;automatic, constraints;microphone, filter, filters;microphone, images;room, impulse, Matched, positions;acoustic, processing;cameras;matched, radiators;array, recovery;beamforming;geometric, response;, response;sound, sensor;acoustic, signal, source;source/receiver, sources;audio},
	doi = {10.1109/ICASSP.2010.5496187},
	author = {O{\textquoteright}Donovan,A.E. and Duraiswami, Ramani and Zotkin,Dmitry N}
}
@conference {14026,
	title = {The manifolds of spatial hearing},
	booktitle = {Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP {\textquoteright}05). IEEE International Conference on},
	volume = {3},
	year = {2005},
	month = {2005/03//},
	pages = {iii/285 - iii/288 Vol. 3 - iii/285 - iii/288 Vol. 3},
	abstract = {We present exploratory studies on learning the non-linear manifold structure, in head related impulse responses (HRIRs). We use the recently popular locally linear embedding technique. The lower dimensional manifold encodes the perceptual information in the HRIRs, namely the direction of the sound source. Based on this, we propose a new method for HRIR interpolation. We also propose that the distance between two HRIRs of an individual be taken as the geodesic distance on the learned manifold.},
	keywords = {acoustics;, dimensional, direction, distance;, EMBEDDING, encoding;, geodesic, Head, hearing, hearing;, HRIR, impulse, information, interpolation;, learned, linear, localization;, locally, low, manifold, manifold;, manifolds;, nonlinear, perceptual, related, response;, responses;, sound, source, spatial, structure;, technique;, transient},
	doi = {10.1109/ICASSP.2005.1415702},
	author = {Duraiswami, Ramani and Raykar,V.C.}
}
