PSD Estimation and Source Separation in a Noisy Reverberant Environment using a Spherical Microphone Array

In this paper, we propose an efficient technique for estimating individual power spectral density (PSD) components, i.e., PSD of each desired sound source as well as of noise and reverberation, in a multi-source reverberant sound scene with coherent background noise. We formulate the problem in the spherical harmonics domain to take the advantage of the inherent orthogonality of the spherical harmonics basis functions and extract the PSD components from the cross-correlation between the different sound field modes. We also investigate an implementation issue that occurs at the nulls of the Bessel functions and offer an engineering solution. The performance evaluation takes place in a practical environment with a commercial microphone array in order to measure the robustness of the proposed algorithm against all the deviations incurred in practice. We also exhibit an application of the proposed PSD estimator through a source septation algorithm and compare the performance with a contemporary method in terms of different objective measures

PSD Estimation of Multiple Sound Sources in a Reverberant Room Using a Spherical Microphone Array

We propose an efficient method to estimate source power spectral densities (PSDs) in a multi-source reverberant environment using a spherical microphone array. The proposed method utilizes the spatial correlation between the spherical harmonics (SH) coefficients of a sound field to estimate source PSDs. The use of the spatial cross-correlation of the SH coefficients allows us to employ the method in an environment with a higher number of sources compared to conventional methods. Furthermore, the orthogonality property of the SH basis functions saves the effort of designing specific beampatterns of a conventional beamformer-based method. We evaluate the performance of the algorithm with different number of sources in practical reverberant and non-reverberant rooms. We also demonstrate an application of the method by separating source signals using a conventional beamformer and a Wiener post-filter designed from the estimated PSDs.Comment: Accepted for WASPAA 201

Spatial dissection of a soundfield using spherical harmonic decomposition

A real-world soundfield is often contributed by multiple desired and undesired sound sources. The performance of many acoustic systems such as automatic speech recognition, audio surveillance, and teleconference relies on its ability to extract the desired sound components in such a mixed environment. The existing solutions to the above problem are constrained by various fundamental limitations and require to enforce different priors depending on the acoustic condition such as reverberation and spatial distribution of sound sources. With the growing emphasis and integration of audio applications in diverse technologies such as smart home and virtual reality appliances, it is imperative to advance the source separation technology in order to overcome the limitations of the traditional approaches. To that end, we exploit the harmonic decomposition model to dissect a mixed soundfield into its underlying desired and undesired components based on source and signal characteristics. By analysing the spatial projection of a soundfield, we achieve multiple outcomes such as (i) soundfield separation with respect to distinct source regions, (ii) source separation in a mixed soundfield using modal coherence model, and (iii) direction of arrival (DOA) estimation of multiple overlapping sound sources through pattern recognition of the modal coherence of a soundfield. We first employ an array of higher order microphones for soundfield separation in order to reduce hardware requirement and implementation complexity. Subsequently, we develop novel mathematical models for modal coherence of noisy and reverberant soundfields that facilitate convenient ways for estimating DOA and power spectral densities leading to robust source separation algorithms. The modal domain approach to the soundfield/source separation allows us to circumvent several practical limitations of the existing techniques and enhance the performance and robustness of the system. The proposed methods are presented with several practical applications and performance evaluations using simulated and real-life dataset

A planar microphone array for spatial coherence-based source separation

We proposed a spatial coherence-based PSD estimation and source separation technique in [1] using a 32-channel spherical microphone array. While the proposed spherical microphone-based method exhibited a satisfactory performance in separating multiple sound sources in a reverberant environment, the use of a large number of microphones remains an issue for some practical considerations. In this paper, we investigate an alternative array structure to achieve spatial coherence-based source separation using a planar microphone array. This method is particularly useful in separating a limited number of sound sources in a mixed acoustic scene. The simplified array structure we used here can easily be integrated with many commercial acoustical instruments such as smart home devices to achieve better speech enhancements.This work is supported by Australian Research Council (ARC) Discovery Projects funding scheme (project no. DP140103412

Estimating the Direct-to-Reverberant Energy Ratio Using a Spherical Harmonics-Based Spatial Correlation Model

The direct-to-reverberant ratio (DRR), which describes the energy ratio between the direct and reverberant component of a soundfield, is an important parameter in many audio applications. In this paper, we present a multichannel algorithm, which utilizes the blind recordings of a spherical microphone array to estimate the DRR of interest. The algorithm is developed based on a spatial correlation model formulated in the spherical harmonics domain. This model expresses the cross correlation matrix of the recorded soundfield coefficients in terms of two spatial correlation matrices, one for direct sound and the other for reverberation. While the direct path arrives from the source, the reverberant path is considered to be a nondiffuse soundfield with varying directional gains. The direct and reverberant sound energies are estimated from the aforementioned spatial correlation model, which then leads to the DRR estimation. The practical feasibility of the proposed algorithm was evaluated using the speech corpus of the acoustic characterization of environments challenge. The experimental results revealed that the proposed method was able to effectively estimate the DRR of a large collection of reverberant speech recordings including various environmental noise types, room types and speakers.DP14010341

Sound Source Localization and Modeling: Spherical Harmonics Domain Approaches

Sound source localization has been an important research topic in the acoustic signal processing community because of its wide use in many acoustic applications, including speech separation, speech enhancement, sound event detection, automatic speech recognition, automated camera steering, and virtual reality. In the recent decade, there is a growing interest in the research of sound source localization using higher-order microphone arrays, which are capable of recording and analyzing the soundfield over a target spatial area. This thesis studies a novel source feature called the relative harmonic coefficient, that easily estimated from the higher-order microphone measurements. This source feature has direct applications for sound source localization due to its sole dependence on the source position. This thesis proposes two novel sound source localization algorithms using the relative harmonic coefficients: (i) a low-complexity single source localization approach that localizes the source' elevation and azimuth separately. This approach is also appliable to acoustic enhancement for the higher-order microphone array recordings; (ii) a semi-supervised multi-source localization algorithm in a noisy and reverberant environment. Although this approach uses a learning schema, it still has a strong potential to be implemented in practice because only a limited number of labeled measurements are required. However, this algorithm has an inherent limitation as it requires the availability of single-source components. Thus, it is unusable in scenarios where the original recordings have limited single-source components (e.g., multiple sources simultaneously active). To address this issue, we develop a novel MUSIC framework based approach that directly uses simultaneous multi-source recordings. This developed MUSIC approach uses robust measurements of relative sound pressure from the higher-order microphone and is shown to be more suitable in noisy environments than the traditional MUSIC method. While the proposed approaches address the source localization problems, in practice, the broader problem of source localization has some more common challenges, which have received less attention. One such challenge is the common assumption of the sound sources being omnidirectional, which is hardly the case with a typical commercial loudspeaker. Therefore, in this thesis, we analyze the broader problem of analyzing directional characteristics of the commercial loudspeakers by deriving equivalent theoretical acoustic models. Several acoustic models are investigated, including plane waves decomposition, point source decomposition, and mixed source decomposition. We finally conduct extensive experimental examinations to see which acoustic model has more similar characteristics with commercial loudspeakers

Reverberant Sound Localization with a Robot Head Based on Direct-Path Relative Transfer Function

International audienceThis paper addresses the problem of sound-source localization (SSL) with a robot head, which remains a challenge in real-world environments. In particular we are interested in locating speech sources, as they are of high interest for human-robot interaction. The microphone-pair response corresponding to the direct-path sound propagation is a function of the source direction. In practice, this response is contaminated by noise and reverberations. The direct-path relative transfer function (DP-RTF) is defined as the ratio between the direct-path acoustic transfer function (ATF) of the two microphones, and it is an important feature for SSL. We propose a method to estimate the DP-RTF from noisy and reverberant signals in the short-time Fourier transform (STFT) domain. First, the convolutive transfer function (CTF) approximation is adopted to accurately represent the impulse response of the microphone array, and the first coefficient of the CTF is mainly composed of the direct-path ATF. At each frequency, the frame-wise speech auto-and cross-power spectral density (PSD) are obtained by spectral subtraction. Then a set of linear equations is constructed by the speech auto-and cross-PSD of multiple frames, in which the DP-RTF is an unknown variable, and is estimated by solving the equations. Finally, the estimated DP-RTFs are concatenated across frequencies and used as a feature vector for SSL. Experiments with a robot, placed in various reverberant environments, show that the proposed method outperforms two state-of-the-art methods

Square root-based multi-source early PSD estimation and recursive RETF update in reverberant environments by means of the orthogonal Procrustes problem

Multi-channel short-time Fourier transform (STFT) domain-based processing of reverberant microphone signals commonly relies on power-spectral-density (PSD) estimates of early source images, where early refers to reflections contained within the same STFT frame. State-of-the-art approaches to multi-source early PSD estimation, given an estimate of the associated relative early transfer functions (RETFs), conventionally minimize the approximation error defined with respect to the early correlation matrix, requiring non-negative inequality constraints on the PSDs. Instead, we here propose to factorize the early correlation matrix and minimize the approximation error defined with respect to the early-correlation-matrix square root. The proposed minimization problem -- constituting a generalization of the so-called orthogonal Procrustes problem -- seeks a unitary matrix and the square roots of the early PSDs up to an arbitrary complex argument, making non-negative inequality constraints redundant. A solution is obtained iteratively, requiring one singular value decomposition (SVD) per iteration. The estimated unitary matrix and early PSD square roots further allow to recursively update the RETF estimate, which is not inherently possible in the conventional approach. An estimate of the said early-correlation-matrix square root itself is obtained by means of the generalized eigenvalue decomposition (GEVD), where we further propose to restore non-stationarities by desmoothing the generalized eigenvalues in order to compensate for inevitable recursive averaging. Simulation results indicate fast convergence of the proposed multi-source early PSD estimation approach in only one iteration if initialized appropriately, and better performance as compared to the conventional approach