2017 25th European Signal Processing Conference (EUSIPCO)

This article develops a semi-supervised algorithm to address the challenging multi-source localization problem in a noisy and reverberant environment, using a spherical harmonics domain source feature of the relative harmonic coefficients . We present a comprehensive research of this source feature, including (i) an illustration confirming its sole dependence on the source position, (ii) a feature estimator in the presence of noise, (iii) a feature selector exploiting its inherent directivity over space. Source features at varied spherical harmonic modes, representing unique characterization of the soundfield, are fused by the Multi-Mode Gaussian Process modeling. Based on the unifying model, we then formulate the mapping function revealing the underlying relationship between the source feature(s) and position(s) using a Bayesian inference approach. Another issue of the overlapped components is addressed by a pre-processing technique performing overlapped frame detection, which in turn reduces this challenging problem to a single source localization. It is highlighted that this data-driven method has a strong potential to be implemented in practice because only a limited number of labeled measurements is required. We evaluate this proposed algorithm using simulated recordings between multiple speakers in diverse environments, and extensive results confirm improved performance in comparison with the state-of-art methods. Additional assessments using real-life recordings further prove the effectiveness of the method, even at unfavorable circumstances with severe source overlapping.

Semi-Supervised Multiple Source Localization Using Relative Harmonic Coefficients Under Noisy and Reverberant Environments

Estimation of the noise power spectral density (PSD) plays a critical role in most existing single-channel speech enhancement algorithms. In this paper, we present a novel noise PSD tracking algorithm, which employs a log-spectral power minimum mean square error (MMSE) estimator. This method updates the noise PSD estimate by performing a temporal recursive averaging of log-spectral MMSE estimate of the current noise power to reduce the risk of speech leakage into noise estimate. A smoothing parameter used in the recursive operation is adjusted by speech presence probability (SPP). In this method, a spectral nonlinear weighting function is derived to estimate the noise spectral power which depends on the a priori and the a posteriori signal-to-noise ratio (SNR). An extensive performance comparison has been carried out with several state-of-the-art noise tracking algorithms, i.e., Minimum Statistics (MS), modified minima controlled recursive averaging algorithm (MCRA-2), MMSE-based method, and SPP-based method. It is clear from experimental results that the proposed algorithm exhibits more excellent noise tracking capability under various nonstationary noise environments and SNR levels. When employed in a speech enhancement framework, improved speech enhancement performance in terms of the segmental SNR (segSNR) improvements and three objective composite metrics is observed.

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Fast Nonstationary Noise Tracking Based on Log-Spectral Power MMSE Estimator and Temporal Recursive Averaging

Fundamental frequency estimation is a very important task in many applications involving periodic signals. For computational reasons, fast autocorrelation-based estimation methods are often used despite parametric estimation methods having superior estimation accuracy. However, these parametric methods are much more costly to run. In this paper, we propose an algorithm which significantly reduces the computational cost of an accurate maximum likelihood-based estimator for real-valued data. The computational cost is reduced by exploiting the matrix structure of the problem and by using a recursive solver. Via benchmarks, we demonstrate that the computation time is reduced by approximately two orders of magnitude. The proposed fast algorithm is available for download online.

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Fast and statistically efficient fundamental frequency estimation

In many spectral estimation and array processing problems, the process of finding estimates of model parameters often involves the optimisation of a cost function containing multiple peaks and dips. Such non-convex problems are hard to solve using traditional optimisation algorithms developed for convex problems, and computationally intensive grid searches are therefore often used instead. In this paper, we establish an analytical connection between the grid size and the parametrisation of the cost function so that the grid size can be selected as coarsely as possible to lower the computation time. Additionally, we show via three common examples how the grid size depends on parameters such as the number of data points or the number of sensors in DOA estimation. We also demonstrate that the computation time can potentially be lowered by several orders of magnitude by combining a coarse grid search with a local refinement step.

/pdf/grid-size-selection-for-nonlinear-least-squares-optimisation-3qiwswszda.pdf

Grid size selection for nonlinear least-squares optimisation in spectral estimation and array processing

Most speech enhancement algorithms need an estimate of the noise power spectral density (PSD) to work. In this paper, we introduce a model-based framework for doing noise PSD estimation. The proposed framework allows us to include prior spectral information about the speech and noise sources, can be configured to have zero tracking delay, and does not depend on estimated speech presence probabilities. This is in contrast to other noise PSD estimators which often have a too large tracking delay to give good results in nonstationary situations and offer no consistent way of including prior information about the speech or the noise type. The results show that the proposed method outperforms state-of-the-art noise PSD estimators in terms of tracking speed and estimation accuracy.

/pdf/model-based-noise-psd-estimation-from-speech-in-non-49pxg8yfns.pdf

Model-Based Noise PSD Estimation from Speech in Non-Stationary Noise

Periodic signals are encountered in many applications. Such signals can be modelled by a weighted sum of sinusoidal components whose frequencies are integer multiples of a fundamental frequency. Given a data set, the fundamental frequency can be estimated in many ways including a maximum likelihood (ML) approach. Unfortunately, the ML estimator has a very high computational complexity, and the more inaccurate, but faster correlation-based estimators are therefore often used instead. In this paper, we propose a fast algorithm for the evaluation of the ML cost function for complex-valued data over all frequencies on a Fourier grid and up to a maximum model order. The proposed algorithm significantly reduces the computational complexity to a level not far from the complexity of the popular harmonic summation method which is an approximate ML estimator.

/pdf/a-fast-algorithm-for-maximum-likelihood-based-fundamental-55do48dev8.pdf

A fast algorithm for maximum likelihood-based fundamental frequency estimation

Knowledge on how a number of loudspeakers are positioned relative to a listening position can be used to enhance the listening experience. Usually, these loudspeaker positions are estimated using calibration signals, either audible or psycho-acoustically hidden inside the desired audio signal. In this paper, we propose to use the desired audio signal instead. Specifically, we treat the case of estimating the distance between two loudspeakers playing back a stereo music or speech signal. In this connection, we develop a real-time maximum likelihood estimator and demonstrate that it has a variance in the millimetre range in a real environment for even a modest sampling frequency.

/pdf/real-time-loudspeaker-distance-estimation-with-stereo-audio-2dalbl1hvl.pdf

Jesper Kjcer Nielsen

Papers

Model-Based Noise PSD Estimation from Speech in Non-Stationary Noise

A fast algorithm for maximum likelihood-based fundamental frequency estimation

Real-time loudspeaker distance estimation with stereo audio