Time delay estimation based on log-sum and l-norm penalized minor component analysis.

Time-delay estimation (TDE), which measures the relative time delay between different receivers, is a fundamental approach for identifying, localizing, and tracking radiating sources. The generalized cross-correlation method is the most popular and is well explained in a landmark paper by Knapp and Carter [(1976). IEEE Trans. Acoust. Speech Signal Process. 24(4), 320-327]. Adaptive eigenvalue decomposition- (EVD) based algorithms have also been developed to improve TDE performance, especially in reverberant environments. This paper extends the adaptive EVD algorithm to utilize the sparsity in transfer channel between source and receivers. Two estimation algorithms based on the log-sum and lp-norm penalized minor component analysis by excitatory and inhibitory learning rules is proposed. In addition, simulations with uncorrelated, correlated noise and reverberation for several signal-to-noise ratios are performed to show the improved estimation performance in noise and reverberation.

[Formula: see text]-regularized recursive total least squares based sparse system identification for the error-in-variables.

In this paper an [Formula: see text]-regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed [Formula: see text]-RTLS algorithm is an RLS like iteration using the [Formula: see text] regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed [Formula: see text]-regularized RTLS for the sparse system identification setting.

Acoustic model for robustness analysis of optimal multipoint room equalization.

In this paper, an acoustic model for the robustness analysis of optimal multipoint room equalization is proposed. The optimal multipoint equalization aims to have the optimal performance in a least-squares sense for all measured points. The model can be used for theoretical robustness estimation depending on the critical design parameters such as the number of measurement points, the distance between measurements, or the frequency before applying real equalization system. The analysis results show that it is important to set the appropriate number of measurement points and the distances between measurement points to ensure the enlarged equalization region at a specific frequency.