System and method for automatic multiple listener room acoustic correction with low filter orders
A system and a methods for correcting, simultaneously at multiple-listener positions, distortions introduced by the acoustical characteristics includes warping room responses, intelligently weighing the warped room acoustical responses to form a weighted response, a low order spectral fitting to the weighted response, forming a warped filter from the low order spectral fit, and unwarping the warped filter to form the room acoustical correction filter.
1. A method for correcting room acoustics at multiple-listener positions, the method comprising:
measuring with a microphone a room acoustical response at each listener position in a multiple-listener environment;
processing each of the room acoustical response measured at said each listener position to obtain non-uniform resolution of the room acoustical response in an audio frequency domain, wherein the non-uniform resolution results in higher resolution at low frequencies for each of the measured room acoustical response;
determining a general response by computing a weighted average of the processed acoustical responses;
generating a low order spectral model of the general response;
obtaining an acoustic correction filter from the low order spectral model, wherein the acoustic correction filter is the inverse of the low order spectral model; and
processing the acoustic correction filter to obtain a room acoustic correction filter with uniform resolution in the audio frequency domain; wherein the room acoustic correction filter corrects the room acoustics at the multiple-listener positions.
2. The method of claim 1 , further comprising generating a stimulus signal for measuring the room acoustical response at each of the listener positions.
3. The method of claim 1 , wherein the general response is determined by a pattern recognition method.
4. The method of claim 3 , wherein the pattern recognition method comprises a method selected from a group consisting of: a hard c-means clustering method, a fuzzy c-means clustering method, and an adaptive learning method.
5. The method of claim 1 , wherein the spectral model comprises a model selected from a group consisting of a Linear Predictive Coding (LPC) model and a pole-zero model.
6. The method of claim 1 , wherein the processing comprises psycho-acoustically motivated warping.
7. The method of claim 6 , wherein the warping is achieved by means of a bilinear conformal map.
8. The method of claim 6 , wherein the psycho-acoustically motivated warping is accomplished in the frequency domain.
9. A method for correcting room acoustics at multiple-listener positions, the method comprising:
measuring with a microphone a room acoustical response at each listener position in a multiple-listener environment;
processing each of the room acoustical response measured at said each listener position to obtain non-uniform resolution of the room acoustical response in an audio frequency domain, wherein the non-uniform resolution results in higher resolution at low frequencies for each of the measured room acoustical response;
obtaining minimum-phase response of each of the said processed acoustical responses;
determining a general response by computing the weighted average of the minimum-phase processed responses;
generating a low order spectral model of the general response;
obtaining an acoustic correction filter from the low order spectral model; and
processing the acoustic correction filter to obtain a room acoustic correction filter with uniform resolution in the audio frequency domain; wherein the room acoustic correction filter corrects the room acoustics at the multiple-listener positions.
10. The method of claim 9 , further comprising generating a stimulus signal for measuring the room acoustical response at each of the listener positions.
11. The method of claim 9 , wherein the general response is determined by a pattern recognition method.
12. The method of claim 11 , wherein the pattern recognition method comprises a method selected from a group consisting of: a hard c-means clustering method, a fuzzy c-means clustering method, and an adaptive learning method.
13. The method of claim 9 , wherein the processing comprises psycho-acoustically motivated warping.
14. The method of claim 13 , wherein the warping is achieved by means of a bilinear conformal map.
15. The method of claim 13 , wherein the psycho-acoustically motivated warping is accomplished in the frequency domain.
16. The method of claim 9 , wherein the spectral model comprises a model selected from a group consisting of a Linear Predictive Coding (LPC) model and a pole-zero model.