Blind Sparse-Nonnegative (BSN) Channel Identification for Acoustic Time-Difference-of-Arrival Estimation

Lin, Yuanqing; Chen, Jingdong; Kim, Youngmoo; Lee, Daniel D.

doi:10.1109/aspaa.2007.4392996

Cited by 13 publications

(44 citation statements)

References 13 publications

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“…To this end, the RIR can be modelled as a sequence of pulses, each one corresponding to the direct path and/or a reflection from a wall with positive amplitude coefficients [17]. Hence, sparsity [14], [16] and non-negativity [15] have been imposed to improve robustness. Even if the sparsity assumption may not hold for the "tail" of the RIR, applications concerning environment geometry [3], [4], [5], [6], [7], [8] require just the recovery of lower order reflections, i.e.…”

Section: Problem Statementmentioning

confidence: 99%

“…A further constraint has been exploited in [15] where the non-negative properties of the RIR [17] are included in the cost function leading to:…”

Section: Problem Statementmentioning

confidence: 99%

“…The second approach avoids these drawbacks since it relies on the implicit properties of the RIR using, in its simplest form, the cross-relation identity in a Single Input Multi Output (SIMO) system that can be easily formulated as an eigenvalue problem [12]. This formalization of the problem has been extended in several ways in order to provide more robustness and to reduce the a priori information required for the solution [14], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

“…The key features of our method are the elimination of strong constraints related to previous approaches (e.g. the anchor constraint [14], [15]) and the ability to promote sparsity and non-negativity to increase robustness and accuracy. Moreover, each iteration of the proposed method results in a convex problem easily solvable with quadratic programming techniques.…”

Section: Introductionmentioning

confidence: 99%

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Room impulse response estimation by iterative weighted L<inf>1</inf>-norm

Crocco

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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This paper presents a novel method to solve for the challenging problem of acoustic Room Impulse Response estimation (RIR). The approach formulates the RIR estimation as a Blind Channel Identification (BCI) problem and it exploits sparsity and non-negativity priors to reduce illposedness and to increase robustness of the solution to noise. This provides an iterative procedure based on a reweighted l 1-norm penalty and a standard l 1-norm constraint. The proposed method guarantees the convexity of the problem at each iteration, it avoids drawbacks related to anchor constraints and it enforces sparsity in a more effective way with respect to standard l 1-norm penalty approaches. Experiments show that our approach outperform current state of the art methods on speech and non-speech real signals.

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Section: Problem Statementmentioning

confidence: 99%

“…A further constraint has been exploited in [15] where the non-negative properties of the RIR [17] are included in the cost function leading to:…”

Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Room impulse response estimation by iterative weighted L<inf>1</inf>-norm

Crocco

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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“…To overcome this problem, methods based on a more realistic signal model have been developed [12,13,14] in which the time delays are computed from the room impulse responses (RIRs) estimated by multichannel blind system identification (BSI). However, the practical use of these methods is limited by the accuracy of multichannel BSI, which is based on cross-relation (CR) error minimization.…”

Section: Introductionmentioning

confidence: 99%

Under-modelled blind system identification for time delay estimation in reverberant environments

Xue

Brookes

Naylor

2016

2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC)

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In multichannel systems, acoustic time delay estimation (TDE) is a challenging problem in reverberant environments. Although blind system identification (BSI) based methods have been proposed which utilize a realistic signal model for the room impulse response (RIR), their TDE performance depends strongly on that of the BSI, which is often inaccurate in practice when the identified responses are under-modelled. In this paper, we propose a new under-modelled BSI based method for TDE in reverberant environments. An under-modelled BSI algorithm is derived, which is based on maximizing the cross-correlation of the cross-filtered signals rather than minimizing the cross-relation error, and also exploits the sparsity of the early part of the RIR. For TDE, this new criterion can be viewed as a generalization of conventional cross-correlationbased TDE methods by considering a more realistic model for the early RIR. Depending on the microphone spacing, only a short early part of each RIR is identified, and the time delays are estimated based on the peak locations in the identified early RIRs. Experiments in different reverberant environments with speech source signals demonstrate the effectiveness of the proposed method.

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