8 research outputs found
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
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
GMM based multi-stage Wiener filtering for low SNR speech enhancement
This paper proposes a single-channel speech enhancement method to reduce the
noise and enhance speech at low signal-to-noise ratio (SNR) levels and
non-stationary noise conditions. Specifically, we focus on modeling the noise
using a Gaussian mixture model (GMM) based on a multi-stage process with a
parametric Wiener filter. The proposed noise model estimates a more accurate
noise power spectral density (PSD), and allows for better generalization under
various noise conditions compared to traditional Wiener filtering methods.
Simulations show that the proposed approach can achieve better performance in
terms of speech quality (PESQ) and intelligibility (STOI) at low SNR levels.Comment: 5 pages, 3 figures, submitted to a conferenc
Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
Spherical harmonic analysis has been a widely used approach for spatial audio processing in recent years. Among all applications that benefit from spatial processing, spatial Active Noise Control (ANC) remains unique with its requirement for open spherical microphone arrays to record the residual sound field throughout the continuous region. Ideally, a low delay spherical harmonic recording algorithm for open spherical microphone arrays is desired for real-time spatial ANC systems. Currently, frequency domain algorithms for spherical harmonic decomposition of microphone array recordings are applied in a spatial ANC system. However, a Short Time Fourier Transform is required, which introduces undesirable system delay for ANC systems. In this paper, we develop a time domain spherical harmonic decomposition algorithm for the application of spatial audio recording mainly with benefit to ANC with an open spherical microphone array. Microphone signals are processed by a series of pre-designed finite impulse response (FIR) filters to obtain a set of time domain spherical harmonic coefficients. The time domain coefficients contain the continuous spatial information of the residual sound field. We corroborate the time domain algorithm with a numerical simulation of a fourth order system, and show the proposed method to have lower delay than existing approaches
Active Noise Control over Space: A Subspace Method for Performance Analysis
In this paper, we investigate the maximum active noise control performance over a three-dimensional (3-D) spatial space, for a given set of secondary sources in a particular environment. We first formulate the spatial active noise control (ANC) problem in a 3-D room. Then we discuss a wave-domain least squares method by matching the secondary noise field to the primary noise field in the wave domain. Furthermore, we extract the subspace from wave-domain coefficients of the secondary paths and propose a subspace method by matching the secondary noise field to the projection of primary noise field in the subspace. Simulation results demonstrate the effectiveness of the proposed algorithms by comparison between the wave-domain least squares method and the subspace method, more specifically the energy of the loudspeaker driving signals, noise reduction inside the region, and residual noise field outside the region. We also investigate the ANC performance under different loudspeaker configurations and noise source positions