Sound source localization and speech enhancement with sparse Bayesian learning beamforming

Abstract

Speech localization and enhancement involves sound source mapping and reconstruction from noisy recordings of speech mixtures with microphone arrays. Conventional beamforming methods suffer from low resolution, especially with a limited number of microphones. In practice, there are only a few sources compared to the possible directions-of-arrival (DOA). Hence, DOA estimation is formulated as a sparse signal reconstruction problem and solved with sparse Bayesian learning (SBL). SBL uses a hierarchical two-level Bayesian inference to reconstruct sparse estimates from a small set of observations. The first level derives the posterior probability of the complex source amplitudes from the data likelihood and the prior. The second level tunes the prior towards sparse solutions with hyperparameters which maximize the evidence, i.e., the data probability. The adaptive learning of the hyperparameters from the data auto-regularizes the inference problem towards sparse robust estimates. Simulations and experimental data demonstrate that SBL beamforming provides high-resolution DOA maps outperforming traditional methods especially for correlated or non-stationary signals. Specifically for speech signals, the high-resolution SBL reconstruction offers not only speech enhancement but effectively speech separation.

Joint Acoustic Localization and Dereverberation Through Plane Wave Decomposition and Sparse Regularization

“…Additionally, ADeLFA is compared with a recently proposed algorithm that also performs joint dereverberation and DOA estimation [24]. In particular, the same acoustic model of ADeLFA can be employed in the algorithm of [24] which essentially utilizes a different strategy to tune the sparse regularization based on sparse Bayesian learning (SBL) to promote spatial sparsity. This algorithm, here referred to as SBL, can be employed using either a single-snapshot (SS) or a multi-snapshot (MS) approach.…”

“…This algorithm, here referred to as SBL, can be employed using either a single-snapshot (SS) or a multi-snapshot (MS) approach. Using the same parameters of the simulation results of [24], the MS approach consists of processing groups of 8 ms time windows with 50% overlap. This results in the estimation of the DOA in longer time windows of 40 ms with 10% overlap.…”

“…Notice that in the following D will indicate the PWDM. Other acoustic models could be employed as well, such as acoustic models of microphones mounted on spherical rigid baffles [18] or of human heads using head-related transfer function (HRTF) [24].…”

“…For example, channel equalization and beamforming are employed in [22], [23] respectively after estimating DOAs using sparse regularization. More recently, in [24] joint dereverberation and DOA estimation has been achieved through a sparse signal reconstruction task. In particular, a beamformer with enhanced resolution is obtained by exploiting sparse Bayesian learning (SBL) to automatically tune the hyperparameters that control the level of sparsity outperforming affirmed beamforming algorithms such as the minimum variance distortionless response (MVDR) [25].…”

“…The formulation of joint DOA estimation and dereverberation through a sound field approximation task allows to propose a new procedure for tuning the level of regularization, which represents the major element of novelty of this paper. In particular, the level of sparsity is not extrapolated from signals statistics as it is commonly pursued in sparsity-based beamforming, like e.g., in [24], but rather by assessing the quality of the approximation through an additional microphone. This is achieved by adopting a modified version of K-fold cross validation (KCV), a procedure often employed in machine learning.…”