The coherent signal subspace method may be used in order to apply subspace localization methods (e.g. MUSIC) to coherent sources. This method involves a focusing process followed by frequency smoothing, which is intended to decorrelate source signals from coherent sources. In practice, however, only moderate decorrelation is obtained, which may lead to performance degradation. Although decorrelation can be improved by widening the smoothing bandwidth, a wider bandwidth may increase focusing error and the smoothing bandwidth is limited by the bandwidth of the actual signal. In this paper, a weighted frequency smoothing that improves decorrelation for a given bandwidth is proposed. It is shown that better decorrelation is obtained by selecting the weights to be inversely proportional to the source signal power at the given frequency. However, since the power of the source is not known, it is estimated by the trace of the array spatial covariance matrix. An experimental study is presented that investigates the effect of the proposed weighting on DOA estimation of speech sources in a reverberant environment.

Estimation of the properties of early room reflections is an important task in audio signal processing, with applications in beamforming, source separation, room geometry inference, and spatial audio. While methods exist to blindly estimate the direction of arrival and delay of the early reflections, the blind estimation of reflection amplitudes remains an open problem. This work presents a preliminary attempt to blindly estimate reflection amplitudes. An iterative estimator is suggested, based on maximum likelihood and alternating least squares. We discuss some fundamental scaling ambiguities of the problem, and show connections between the proposed method and raking beamformers. A simulation study demonstrates the effectiveness of the proposed method.

Acoustic rake filters perform coherent summation of the early room reflections using beamforming, with the aim of improving beamforming performance. This concept has been investigated for speech enhancement applications, improving noise reduction and late reverberation attenuation. Current studies typically assume that the parameters of the early reflections, such as the direction-of-arrival, delay and amplitude, are known in advance in the rake filter design. This work presents a novel investigation of the acoustic rake filter in a more practical context, focusing on the minimum variance distortionless response (MVDR) formulation. First, the sensitivity of the filter performance to perturbations in the reflection parameters is derived analytically, and investigated numerically using Monte Carlo simulations with a spherical microphone array. Then, an end-to-end example of rake filtering in a blind scenario is presented, where the reflection parameters are estimated from speech signals without any prior information. This example demonstrates for the first time the use of rake filtering in a realistic scenario.