Optimized gradient calculation for room impulse response reshaping algorithm based on p-norm optimization

Mazur, Radoslaw; Jungmann, Jan Ole; Mertins, Alfred

doi:10.1109/icassp.2012.6287848

Cited by 7 publications

(14 citation statements)

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“…In RIR shortening, the attenuation of the original RIR is accelerated so that the reverberation effect is weakened. Different techniques have been proposed in the literature [78,[122][123][124][125][126][127][128][129][130]. In what follows, we review the most relevant methods.…”

Section: Room Impulse Response Reshapingmentioning

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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Section: Room Impulse Response Reshapingmentioning

confidence: 99%

Room Response Equalization—A Review

2017

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“…For speech communication, where acoustic echo cancellation (AEC) has become a mandatory feature, as well as for reproduction-only scenarios, a fast and robust estimation of acoustic impulse responses (AIRs) is essential. The extension to multichannel systems leads to adaptive solutions like stereophonic AEC (SAEC) [1], [2], [3], [4], [5] in speech communication or room equalization [6], [7], [8], sound focusing [9], [10], cross-talk cancellation [11], [12], [13], and combinations thereof [14] in reproduction applications. Although it is often possible to measure and store AIRs for a given MISO system setup in a calibration phase, this solution will not be robust to changes in the acoustic conditions.…”

Section: Introductionmentioning

confidence: 99%

Trends in adaptive MISO system identification for multichannel audio reproduction and speech communication

Thune

Enzner

2013

2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA)

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Online identification of multiple-input / single-output (MISO) acoustic systems is one of the long-standing and continuing challenges in multichannel speech and audio applications. Fast and robust estimation of the impulse response of an acoustic system is a key requirement for several adaptive solutions in time-varying scenarios, such as stereophonic acoustic echo cancellation, room equalization, or crosstalk cancellation. The inevitable presence of cross-correlated loudspeaker signals that is implied by multichannel applications, however, entails the well-known non-uniqueness problem of MISO system identification. Apart from this fundamental issue, a more practical problem already consists in the lack of techniques to evaluate the estimated impulse responses properly. Since well-established measures are often not capable of accounting for all aspects of online MISO system identification, we revert to the recently proposed spectral-importance weighted misalignment (SIWM) to assess MISO identification. In this contribution, we review SIWM and its relation to well-established evaluation tools. On this basis, we provide an insight into the problem of MISO system identification in applications driven by real stereo data. We also analyze and compare a traditional and a very recent approach to deal with the non-uniqueness problem.

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“…The choice of the norm is critical, as it affects both the nature of the errors and the computational efficiency. The previous works in shaping use either an ∞ norm 1 [27,31] or an p norm 2 [21,28,29,32] and use either interior point methods [27] that are memory exhaustive or gradient descent approaches [21,28,29,32] that are computationally very slow. This thesis uses a regularized [33] formulation of the shaping problem which minimizes a combination of a norm of the weighted error and a regularizer of the shaping filters.…”

Section: Motivationmentioning

confidence: 99%

“…The minimax approach of [20] converges even more slowly as each iteration only modifies a single tap of the shortening filter. A modification to the steepest descent update rule was made that allowed the algorithm to converge faster and be less likely trapped in a local minima [32]. This modification was based upon a diagonal approximation of the Hessian matrix, used in the steepest descent iteration.…”

Section: Impulse Response Shaping (Or Shortening)mentioning

confidence: 99%

“…The DALM algorithm has been originally proposed to provide a sparse solution for the 1 regularised 2 minimizations (D-2-1) as in the Basis Pursuit DeNoising (BPDN) [37] problems that arise in sparse reconstruction. The steepest descent approaches in [20,21,32] choose a suitably varying step size parameter, which requires a line search algorithm [41, p. 464-466]. Even then an unacceptable number of iterations may be required for convergence, especially for very large problems which are usually poorly conditioned.…”

Section: Impulse Response Shaping (Or Shortening)mentioning

confidence: 99%

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Acoustic Room Impulse Response Shaping

Krishnan¹

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<p>Impulse response shaping is a technique for modifying the characteristics of a linear channel to achieve desirable characteristics. The technique is well-known in the field of wireless communication. Acoustic impulse response shaping is used to reduce the effects of reverberation on audio signals propagating inside a room and is thus used for listening room compensation. This thesis addresses innovative approaches for acoustic impulse response shaping. Many techniques have been proposed in the literature for canceling or reducing the effect of reverberation on the audio signal. Impulse response inversion attempts to completely cancel the effect of reverberations whereas impulse response shortening (or shaping) only partly equalizes the room impulse responses. Shortening has less stringent constraints than inversion and this can result in more robust solutions and thus more practically realizable systems. Acoustic impulse response shaping works on measured room impulse responses and designs pre-filters to be placed before the loudspeakers so that the reverberation is reduced at the listening positions. When sampled, the room responses typically contain thousands or tens of thousands (N ) of samples. Thus, the shaping algorithm needs to be computationally fast and memory efficient in order to implement the system in real time. The techniques presented in the literature use interior point methods or steepest descent algorithms, which are computationally slow or require memory of the order of N² . This thesis presents shaping approaches based on the Dual Augmented Lagrangian Method (DALM), known in the literature on sparse reconstruction for its super-linear convergence. The method presented here also makes use of the concept of a Forward Adjoint Oracle (FAO) to make the shaping algorithm memory efficient. Thus, the thesis presents computationally fast and memory efficient shaping algorithms that can be used for practically realizable systems. The thesis also presents robust shaping approaches. The measured room responses may contain measurement errors or noise and can vary from time to time. These variations may be due to changes in atmospheric conditions (such as temperature or humidity) or due to change in position of objects inside a room. While design approaches over multiple microphone positions have been proposed for design of filters that are robust to change in microphone positions, a more rigorous approach is statistical, involving the inclusion of some statistical constraints into the optimization problem. The thesis presents both the approaches viz., a computationally faster version (using DALM) of the already proposed design over multiple positions and a statistically robust shaping formulation. The latter limits the probability of large errors between expected and obtained response to be less than a specified value. This ensures that the solution is robust to variations in the room response. The shaping algorithm works in the time domain, shaping the temporal characteristics of the room response to a desired form. The frequency response of the shaped response can contain potentially undesirable peaks and troughs. This thesis therefore presents an approach for an efficient projection to improve spectral flatness of the resultant response. This algorithm can be combined with the fast and memory efficient DALM based approach to achieve joint time and frequency shaping. Finally, the thesis also presents a computationally fast algorithm based on DALM for pressure matching used in sound field reproduction. Impulse response shaping is applied in sound field reproduction, showing that the levels of pre-reverberation induced by a temperature change can be reduced. This application is different from impulse response shaping approaches presented in the previous chapters and highlights the flexibility of the algorithm developed in this thesis and its wide range of applications.</p>

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Optimized gradient calculation for room impulse response reshaping algorithm based on p-norm optimization

Cited by 7 publications

References 8 publications

Room Response Equalization—A Review

Room Response Equalization—A Review

Trends in adaptive MISO system identification for multichannel audio reproduction and speech communication

Acoustic Room Impulse Response Shaping

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