Oliver Thiergart

Signal-to-reverberant ratio estimation based on the complex spatial coherence between omnidirectional microphones

The signal-to-reverberant ratio (SRR) is an important parameter in several applications such as speech enhancement, dereverberation, and parametric spatial audio coding. In this contribution, an SRR estimator is derived from the direction-of-arrival dependent complex spatial coherence function computed via two omnidirectional microphones. It is shown that by employing a computationally inexpensive DOA estimator, the proposed SRR estimator outperforms existing approaches.

An informed LCMV filter based on multiple instantaneous direction-of-arrival estimates

Extracting sound sources in noisy and reverberant conditions remains a challenging task that is commonly found in modern communication systems. In this work, we consider the problem of obtaining a desired spatial response for at most L simultaneously active sound sources. The proposed spatial filter is obtained by minimizing the diffuse plus self-noise power at the output of the filter subject to L linear constraints. In contrast to earlier works, the L constraints are based on instantaneous narrowband direction-of-arrival estimates. In addition, a novel estimator for the diffuse-to-noise ratio is developed that exhibits a sufficiently high temporal and spectral resolution to achieve both dereverberation and noise reduction. The presented results demonstrate that an optimal tradeoff between maximum white noise gain and maximum directivity is achieved.

An informed parametric spatial filter based on instantaneous direction-of-arrival estimates

Extracting desired source signals in noisy and reverberant environments is required in many hands-free communication systems. In practical situations, where the position and number of active sources may be unknown and time-varying, conventional implementations of spatial filters do not provide sufficiently good performance. Recently, informed spatial filters have been introduced that incorporate almost instantaneous parametric information on the sound field, thereby enabling adaptation to new acoustic conditions and moving sources. In this contribution, we propose a spatial filter which generalizes the recently proposed informed linearly constrained minimum variance filter and informed minimum mean square error filter. The proposed filter uses multiple direction-of-arrival estimates and second-order statistics of the noise and diffuse sound. To determine those statistics, an optimal diffuse power estimator is proposed that outperforms state-of-the-art estimators. Extensive performance evaluation demonstrates the effectiveness of the proposed filter in dynamic acoustic conditions. For this purpose, we have considered a challenging scenario which consists of quickly moving sound sources during double-talk. The performance of the proposed spatial filter was evaluated in terms of objective measures including segmental signal-to-reverberation ratio and log spectral distance, and by means of a listening test confirming the objective results.

On the spatial coherence in mixed sound fields and its application to signal-to-diffuse ratio estimation

Many applications in spatial sound recording and processing model the sound scene as a sum of directional and diffuse sound components. The power ratio between both components, i.e., the signal-to-diffuse ratio (SDR), represents an important measure for algorithms which aim at performing robustly in reverberant environments. This contribution discusses the SDR estimation from the spatial coherence between two arbitrary first-order directional microphones. First, the spatial coherence is expressed as function of the SDR. For most microphone setups, the spatial coherence is a complex function where both the absolute value and phase contain relevant information on the SDR. Secondly, the SDR estimator is derived from the spatial coherence function. The estimator is discussed for different practical microphone setups including coincident setups of arbitrary first-order directional microphones and spaced setups of identical first-order directional microphones. An unbiased SDR estimation requires noiseless coherence estimates as well as information on the direction-of-arrival of the directional sound, which usually has to be estimated. Nevertheless, measurement results verify that the proposed estimator is applicable in practice and provides accurate results.

The diffuse sound field in energetic analysis

Measuring the degree of diffuseness of a sound field is crucial in many modern parametric spatial audio techniques. In these applications, intensity-based diffuseness estimators are particularly convenient, as the sound intensity can also be used to obtain, e.g., the direction of arrival of the sound. This contribution reviews different diffuseness estimators comparing them under the conditions found in practice, i.e., with arrays of noisy microphones and with the expectation operators substituted by finite temporal averages. The estimators show a similar performance, however, each with specific advantages and disadvantages depending on the scenario. Furthermore, the paper derives an estimator and highlights the possibility of using spatial averaging to improve the temporal resolution of the estimates.

Generating virtual microphone signals using geometrical information gathered by distributed arrays

Conventional recording techniques for spatial audio are limited to the fact that the spatial image obtained is always relative to the position in which the microphones have been physically placed. In many applications, however, it is desired to place the microphones outside the sound scene and yet be able to capture the sound from an arbitrary perspective. This contribution proposes a method to place a virtual microphone at an arbitrary point in space, by computing a signal perceptually similar to the one which would have been picked up if the microphone had been physically placed in the sound scene. The method relies on a parametric model of the sound field based on point-like isotropic sound sources. The required geometrical information is gathered by two or more distributed microphone arrays. Measurement results demonstrate the applicability of the proposed method and reveal its limitations.

Coherence-based diffuseness estimation in the spherical harmonic domain

The diffuseness of sound fields has previously been estimated in the spatial domain using the spatial coherence between a pair of microphones (omnidirectional or first-order). In this paper, we propose a diffuseness estimator for spherical microphone arrays based on the coherence between eigenbeams, which result from a spherical harmonic decomposition of the sound field. The weighted averaging of the diffuseness estimates over all eigenbeam pairs is shown to significantly reduce the variance of the estimates, particularly in fields with low diffuseness.

Parametric Spatial Sound Processing: A flexible and efficient solution to sound scene acquisition, modification, and reproduction

Flexible and efficient spatial sound acquisition and subsequent processing are of paramount importance in communication and assisted listening devices such as mobile phones, hearing aids, smart TVs, and emerging wearable devices (e.g., smart watches and glasses). In application scenarios where the number of sound sources quickly varies, sources move, and nonstationary noise and reverberation are commonly encountered, it remains a challenge to capture sounds in such a way that they can be reproduced with a high and invariable sound quality. In addition, the objective in terms of what needs to be captured, and how it should be reproduced, depends on the application and on the user?s preferences. Parametric spatial sound processing has been around for two decades and provides a flexible and efficient solution to capture, code, and transmit, as well as manipulate and reproduce spatial sounds.

Geometry-Based Spatial Sound Acquisition Using Distributed Microphone Arrays

Traditional spatial sound acquisition aims at capturing a sound field with multiple microphones such that at the reproduction side a listener can perceive the sound image as it was at the recording location. Standard techniques for spatial sound acquisition usually use spaced omnidirectional microphones or coincident directional microphones. Alternatively, microphone arrays and spatial filters can be used to capture the sound field. From a geometric point of view, the perspective of the sound field is fixed when using such techniques. In this paper, a geometry-based spatial sound acquisition technique is proposed to compute virtual microphone signals that manifest a different perspective of the sound field. The proposed technique uses a parametric sound field model that is formulated in the time-frequency domain. It is assumed that each time-frequency instant of a microphone signal can be decomposed into one direct and one diffuse sound component. It is further assumed that the direct component is the response of a single isotropic point-like source (IPLS) of which the position is estimated for each time-frequency instant using distributed microphone arrays. Given the sound components and the position of the IPLS, it is possible to synthesize a signal that corresponds to a virtual microphone at an arbitrary position and with an arbitrary pick-up pattern.

Power-based signal-to-diffuse ratio estimation using noisy directional microphones

The signal-to-diffuse ratio (SDR), which describes the power ratio between the direct and diffuse component of a sound field, is an important parameter in many applications. This paper proposes a power-based SDR estimator which considers the auto power spectral densities obtained by noisy directional microphones. Compared to recently proposed estimators that exploit the spatial coherence between two microphones, the power-based estimator is more robust at lower frequencies given that the microphone directivities are known with sufficiently high accuracy. The proposed estimator can incorporate more than two microphones and can therefore provide accurate SDR estimates independently of the direction-of-arrival of the direct sound. We further propose a method to determine the optimal microphone orientations for a given set of directional microphones. Simulations show the practical applicability.