New Factors in Room Equalization Using a Fuzzy Logic Approach (bibtex)
by Bharitkar, Sunil and Kyriakakis, Chris
Abstract:
Room acoustical modes, particularly in small rooms, cause a significant variation in the room responses measured at di!erent locations. Responses measured only a few cm apart can vary by up to 15-20 dB at certain frequencies. This makes it diffcult to equalize an audio system for multiple simultaneous listeners. Previous methods have utilized multiple microphones and spatial averaging with equal weighting. In this paper we present a different multiple point equalization method. We first determine representative prototypical room responses derived from several room responses that share similar characteristics, using the fuzzy unsupervised learning method. These prototypical responses can then be combined to form a general point response. When we use the inverse of the general point response as an equalizing filter, our results show a significant improvement in equalization performance over the spatial averaging methods. This simultaneous equalization is achieved by suppressing the peaks in the room magnitude spectrums. Applications of this method thus include equalization and multiple point sound control at home and in automobiles.
Reference:
New Factors in Room Equalization Using a Fuzzy Logic Approach (Bharitkar, Sunil and Kyriakakis, Chris), In Proceedings of the Audio Engineering Society Convention, 2001.
Bibtex Entry:
@inproceedings{bharitkar_new_2001,
	address = {New York, NY},
	title = {New {Factors} in {Room} {Equalization} {Using} a {Fuzzy} {Logic} {Approach}},
	url = {http://ict.usc.edu/pubs/New%20Factors%20in%20Room%20Equalization%20Using%20a%20Fuzzy%20Logic%20Approach.pdf},
	abstract = {Room acoustical modes, particularly in small rooms, cause a significant variation in the room responses measured at di!erent locations. Responses measured only a few cm apart can vary by up to 15-20 dB at certain frequencies. This makes it diffcult to equalize an audio system for multiple simultaneous listeners. Previous methods have utilized multiple microphones and spatial averaging with equal weighting. In this paper we present a different multiple point equalization method. We first determine representative prototypical room responses derived from several room responses that share similar characteristics, using the fuzzy unsupervised learning method. These prototypical responses can then be combined to form a general point response. When we use the inverse of the general point response as an equalizing filter, our results show a significant improvement in equalization performance over the spatial averaging methods. This simultaneous equalization is achieved by suppressing the peaks in the room magnitude spectrums. Applications of this method thus include equalization and multiple point sound control at home and in automobiles.},
	booktitle = {Proceedings of the {Audio} {Engineering} {Society} {Convention}},
	author = {Bharitkar, Sunil and Kyriakakis, Chris},
	month = sep,
	year = {2001}
}
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