Multiple position room response equalization: Frequency domain prototype design strategies

Abstract

In this paper we consider an alternative to the multiple position room response equalization technique developed by Bharitkar and Kyriakakis. In fact, the original approach proposed by Bharitkar and Kyriakakis works in the time domain and employs a fuzzy c-means clustering algorithm for combining multiple measures of the room response into a single prototype response used for the equalizer design. Recently, by working in the frequency domain we have derived an efficient implementation of the room response equalization technique that avoids many onerous operations of the approach of Bharitkar and Kyriakakis. In this paper, we discuss different techniques that can replace the fuzzy c-means clustering algorithm in the derivation of the prototype room response. The techniques we discuss naturally arise from the frequency domain implementation of the room response equalization system. In addition, they have a much lower computational complexity than the fuzzy c-means clustering. In all our experiments, the equalization performances of the system equipped with the proposed prototype design techniques have been at least similar to those of the original approach of Bharitkar and Kyriakakis.

Room Response Equalization—A Review

“…The all-pole model can be obtained by different techniques, including the efficient Levison-Durbin algorithm [62]. LPC analysis has been one of the most successful approaches for minimum-phase equalization, and has been successfully used by many researchers [61,[63][64][65][66][67][68][69][70][71][72][73][74][75]. An all-pole filter can adequately model the spectral peaks of the room response, while it provides a less accurate model of the notches.…”

“…Figure 11 shows the curves used for the S D calculation. A Mean Spectral Deviation Measure (MSDM) that represents the mean value of the final spectral deviation measures over the entire set of measured RIRs has also been considered [74,146]. In analogy to the mean spectral deviation measure, which gives a measure of the deviation of the magnitude frequency response from a flat one [228], a mean group delay deviation measure was introduced in [147,229] to quantify the average variation in terms of group delay:…”