Perceptual multiple location equalization with clustering

Bharitkar, Sunil; Kyriakakis, Chris

doi:10.1109/acssc.2002.1197171

Cited by 12 publications

(10 citation statements)

References 9 publications

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“…The minimum-phase part of the warped room impulse responses is extracted. In [3], the authors suggested to compute the minimum-phase sequences from cepstrum, which requires the computation of two FFTs and two IFFTs on N samples, N logarithms and N exponentiations for each of the M impulse responses. The minimum-phase extraction is important for the clustering operation, in order to avoid undesirable time and frequency domain effects, due to incoherent linear combination [4].…”

Section: Original Methods In Time Domainmentioning

confidence: 99%

Multiple position room response equalization: Frequency domain prototype design strategies

Carini

Omiciuolo

Sicuranza

2009

2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis

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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.

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Section: Original Methods In Time Domainmentioning

confidence: 99%

Multiple position room response equalization: Frequency domain prototype design strategies

Carini

Omiciuolo

Sicuranza

2009

2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis

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“…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.…”

Section: Lpc Analysismentioning

confidence: 99%

“…A psychoacoustically-motivated frequency scale-in most cases the Bark scale-is used. For example, in the approach of [70,71], a prototype room response is first frequency warped to an approximate Bark scale. Then, an all-pole model of the room response is obtained in the warped domain using LPC analysis.…”

Section: Frequency Warpingmentioning

confidence: 99%

Room Response Equalization—A Review

2017

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Room response equalization aims at improving the sound reproduction in rooms by applying advanced digital signal processing techniques to design an equalizer on the basis of one or more measurements of the room response. This topic has been intensively studied in the last 40 years, resulting in a number of effective techniques facing different aspects of the problem. This review paper aims at giving an overview of the existing methods following their historical evolution, and discussing pros and cons of each approach with relation to the room characteristics, as well as instrumental and perceptual measures. The review is concluded by a discussion on emerging topics and new trends. This paper aims to provide an up-to-date review on RRE, discussing the pros and the cons of each technique, following the historical evolution. It is worth underlining that the RRE problem is analyzed from the viewpoint of impulse response analysis. All approaches that are not directly based on RIR analysis (e.g., parametric or graphic equalizers) are not discussed. The reader is referred to [11] for a comprehensive review on this topic. Another research field related to RRE which is not addressed in this paper is sound spatialization. The reader is referred to [12] for a recent review.This review article is organized as follows: Section 2 describes the characteristics of room impulse responses and its perception by the human auditory system. Section 3 introduces the basic concept of RRE, explaining the main challenges in inverting room responses. Section 4 describes the approaches used for equalizer design following their historical evolution. Section 5 discusses pre-processing techniques used to cope for RIR variations by exploiting human perception. Section 6 covers the evolution from single-point to multi-point equalization using multiple microphones placed within the room. Section 7 reports adaptive approaches for RRE in the framework of single-point and multi-point equalization. Section 8 introduces innovative approaches following a wave-theoretical view on the problem. Section 9 describes instrumental and perceptual measures used for state-of-the-art evaluation of RRE approaches. Section 10 reports emerging methods and new trends in the field. Finally, Section 11 concludes this review. The Room Response and Its PerceptionThe characteristics of the room response in the time and frequency domain are related to the acoustic properties of the environment that influence human perception. Due to this aspect, it is sensible to shape the impulse response analysis in order to handle important issues that should be considered in the RRE procedure to reach a sound listening improvement. This includes knowledge on human perception and psychoacoustics to be exploited explicitly in the equalization procedure. Appl. Sci. 2018, 8, 16 4 of 47An impulse response, obtained from a sound source in a specific position of a real environment, can be divided into three parts [13]: (i) direct sound; (ii) early reflections, and (iii) late reflections, as r...

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“…Several observations can be made: ( I ) The pattem recognition based filter outperforms the R M S averaging filters within the framework of performing warping (using the method of [9]) to the Bark scale. (2) The RMS average methods lose their phase information (see (5)). hence cannot take advantage of warping methods that use all-pass filters, since this kind of warping between the axes (viz., Fig.…”

Section: Discussionmentioning

confidence: 99%

“…Accordingly, in our previous papers [31, [41, IS], [21 we have proposed new approaches for designing a multiple location equalizaa tion filter. Specifically, the filter in [2] was designed by (i) warping the room responses to the Bark scale using the concepts from [91.…”

Section: Introductionmentioning

confidence: 99%

A comparison between multi-channel audio equalization filters using warping

Bharitkar

Kyriakakis

2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684)

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Typically, mom equalization techniques do not focus on designing filters that equalize Ute mom responses at perceptually relevant frequencies. Thus, by performing Bark warping 01 Ute room responses and using lower order spectral models it is possible to design low order psycho-acoustically motivated equalization filters. In this paper, we compare the performance, thmugh experiments, between the traditional RhlS averaging filter (with and without warping lo the Bark scale) and our pattern recognition based multiple listener equalization filter with warping 121. It is s h o w that the our pattern recognition filter, using warping, outperforms the RMS averaging filter (with and n,ithout warping to the Bark scale).

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Perceptual multiple location equalization with clustering

Cited by 12 publications

References 9 publications

Multiple position room response equalization: Frequency domain prototype design strategies

Multiple position room response equalization: Frequency domain prototype design strategies

Room Response Equalization—A Review

A comparison between multi-channel audio equalization filters using warping

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