Room impulse response estimation by iterative weighted L1-norm
نویسندگان
چکیده
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 l1-norm penalty and a standard l1-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 l1-norm penalty approaches. Experiments show that our approach outperform current state of the art methods on speech and non-speech real signals.
منابع مشابه
Estimation of TDOA for room reflections by iterative weighted l1 constraint
Estimation of Time Difference of Arrivals (TDOAs) corresponding to early room reflections can be formulated as a Blind Channel Identification (BCI) problem, exploiting signals acquired by a set of microphones, given an unknown transmitted source. To cope with the intrinsic noise sensitivity and ill-posedness of the problem, sparsity and nonnegativity priors on the Acoustic Impulse Response (AIR...
متن کاملPeak-error-constrained sparse FIR filter design using iterative L1 optimization
In this paper, a novel algorithm is presented for the design of sparse linear-phase FIR filters. Compared to traditional l1-optimization-based methods, the proposed algorithm minimizes l1 norm of a portion (instead of all) of nonzero coefficients. In this way, some nonzero coefficients at crucial positions are not affected by l1 norm utilized in the objective function. The proposed algorithm em...
متن کاملSparse channel estimation with lp-norm and reweighted l1-norm penalized least mean squares
The least mean squares (LMS) algorithm is one of the most popular recursive parameter estimation methods. In its standard form it does not take into account any special characteristics that the parameterized model may have. Assuming that such model is sparse in some domain (for example, it has sparse impulse or frequency response), we aim at developing such LMS algorithms that can adapt to the ...
متن کاملEqualizer-Aided Time Delay Tracking Based on L1-Normed Finite Differences
This paper addresses the estimation of time delay between two spatially separated noisy signals by system identification modeling with the input and output corrupted by additive white Gaussian noise. The proposed method is based on a modified adaptive Butler-Cantoni equalizer that decouples noise variance estimation from channel estimation. The bias in time delay estimates that is induced by in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2015