Alastair H. Moore

The ACE challenge — Corpus description and performance evaluation

Knowledge of the Direct-to-Reverberant Ratio (DRR) and Reverberation Time (T60) can be used to better perform speech and audio processing such as dereverberation. Established methods compute these parameters from measured Acoustic Impulse Responses (AIRs). However, in many practical situations the AIR is not available and the parameters must be estimated non-intrusively directly from noisy speech or audio signals. The Acoustic Characterization of Environments (ACE) Challenge is a competition to identify the most promising non-intrusive DRR and T60 estimation methods using real noisy reverberant speech. We describe the ACE corpus comprising multi-channel AIRs, and multi-channel noise including ambient, fan and babble noise recorded in the same environment as the measured AIRs, along with the corresponding DRR and T60 measurements. The evaluation methodology is discussed and comparative results are shown.

Multiple source localisation in the spherical harmonic domain

Spherical arrays facilitate processing and analysis of sound fields with the potential for high resolution in three dimensions in the spherical harmonic domain. Using the captured sound field, robust source localisation systems are required for speech acquisition, speaker tracking and environment mapping. Source localisation becomes a challenging problem in reverberant environments and under noisy conditions, leading to potentially poor performance in cocktail party scenarios. This paper evaluates the performance of a low-complexity localisation approach using spherical harmonics in reverberant environments for multiple speakers. Eigen-beams are used to estimate pseudo-intensity vectors pointing in the direction of sound intensity. This paper proposes a clustering approach in which the intensity vectors of active sound sources and strong reflections are extracted, yielding an estimate of the source direction in azimuth and inclination as an approach to source localisation.

Direction of arrival estimation using pseudo-intensity vectors with direct-path dominance test

The accuracy of direction of arrival estimation tends to degrade under reverberant conditions due to the presence of reflected signal components which are correlated with the direct path. The recently proposed direct-path dominance test provides a means of identifying time-frequency regions in which a single signal path is dominant. By analysing only these regions it was shown that the accuracy of the FS-MUSIC algorithm could be significantly improved. However, for real-time implementation a less computationally demanding localisation algorithm would be preferable. In the present contribution we investigate the direct-path dominance test as a preprocessing step to pseudo-intensity vector-based localisation. A novel formulation of the pseudo-intensity vector is proposed which further exploits the direct path dominance test and leads to improved localisation performance.

Acoustic simultaneous localization and mapping (A-SLAM) of a moving microphone array and its surrounding speakers

Acoustic scene mapping creates a representation of positions of audio sources such as talkers within the surrounding environment of a microphone array. By allowing the array to move, the acoustic scene can be explored in order to improve the map. Furthermore, the spatial diversity of the kinematic array allows for estimation of the source-sensor distance in scenarios where source directions of arrival are measured. As sound source localization is performed relative to the array position, mapping of acoustic sources requires knowledge of the absolute position of the microphone array in the room. If the array is moving, its absolute position is unknown in practice. Hence, Simultaneous Localization and Mapping (SLAM) is required in order to localize the microphone array position and map the surrounding sound sources. In realistic environments, microphone arrays receive a convolutive mixture of direct-path speech signals, noise and reflections due to reverberation. A key challenge of Acoustic SLAM (a-SLAM) is robustness against reverberant clutter measurements and missing source detections. This paper proposes a novel bearing-only a-SLAM approach using a Single-Cluster Probability Hypothesis Density filter. Results demonstrate convergence to accurate estimates of the array trajectory and source positions.

Estimation of Room Acoustic Parameters: The ACE Challenge

Reverberation time (T60) and Direct-to-reverberant ratio (DRR) are important parameters which together can characterize sound captured by microphones in nonanechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of T60 and DRR can be estimated directly from the acoustic impulse response (AIR) of the room. In practice, the AIR is not normally available, in which case these parameters must be estimated blindly from the observed speech in the microphone signal. The acoustic characterization of environments (ACE) challenge aimed to determine the state-of-the-art in blind acoustic parameter estimation and also to stimulate research in this area. A summary of the ACE challenge, and the corpus used in the challenge is presented together with an analysis of the results. Existing algorithms were submitted alongside novel contributions, the comparative results for which are presented in this paper. The challenge showed that T60 estimation is a mature field where analytical approaches dominate whilst DRR estimation is a less mature field where machine learning approaches are currently more successful.

Spherical microphone array acoustic rake receivers

Several signal independent acoustic rake receivers are proposed for speech dereverberation using spherical microphone arrays. The proposed rake designs take advantage of multipaths, by separately capturing and combining early reflections with the direct path. We investigate several approaches in combining reflections with the direct path source signal, including the development of beam patterns that point nulls at all preceding reflections. The proposed designs are tested in experimental simulations and their dereverberation performances evaluated using objective measures. For the tested configuration, the proposed designs achieve higher levels of dereverberation compared to conventional signal independent beamforming systems; achieving up to 3.6 dB improvement in the direct-to-reverberant ratio over the plane-wave decomposition beamformer.

Direction of Arrival Estimation in the Spherical Harmonic Domain Using Subspace Pseudointensity Vectors

Direction of arrival (DOA) estimation is a fundamental problem in acoustic signal processing. It is used in a diverse range of applications, including spatial filtering, speech dereverberation, source separation and diarization. Intensity vector-based DOA estimation is attractive, especially for spherical sensor arrays, because it is computationally efficient. Two such methods are presented that operate on a spherical harmonic decomposition of a sound field observed using a spherical microphone array. The first uses pseudointensity vectors (PIVs) and works well in acoustic environments where only one sound source is active at any time. The second uses subspace pseudointensity vectors (SSPIVs) and is targeted at environments where multiple simultaneous soures and significant levels of reverberation make the problem more challenging. Analytical models are used to quantify the effects of an interfering source, diffuse noise, and sensor noise on PIVs and SSPIVs. The accuracy of DOA estimation using PIVs and SSPIVs is compared against the state of the art in simulations including realistic reverberation and noise for single and multiple, stationary and moving sources. Finally, robust performance of the proposed methods is demonstrated by using speech recordings in a real acoustic environment.

3D acoustic source localization in the spherical harmonic domain based on optimized grid search

An approach for 3D source localization using a spherical microphone array is proposed that gives improved accuracy compared to intensity-based methods. First order spherical harmonics are first used to obtain an initial approximate localization result and then the initial result is improved based on an optimized grid search in the local vicinity using the method of least squares and high-order spherical harmonics. We show that this approach outperforms the first-order approach and shows strong robustness to reverberation and noise. The worst average error of 3 degrees was found in our experiments in the presence of realistic reverberation and noise.

Bearing-only acoustic tracking of moving speakers for robot audition

This paper focuses on speaker tracking in robot audition for human-robot interaction. Using only acoustic signals, speaker tracking in enclosed spaces is subject to missing detections and spurious clutter measurements due to speech inactivity, reverberation and interference. Furthermore, many acoustic localization approaches estimate speaker direction, hence providing bearing-only measurements without range information. This paper presents a probability hypothesis density (PHD) tracker that augments the bearing-only speaker directions of arrival with a cloud of range hypotheses at speaker initiation and propagates the random variates through time. Furthermore, due to their formulation PHD filters explicitly model, and hence provide robustness against, clutter and missing detections. The approach is verified using experimental results.

Direct-to-Reverberant Ratio estimation using a null-steered beamformer

Reverberation affects the quality and intelligibility of distant speech recorded in a room. Direct-to-Reverberant Ratio (DRR) is a useful measure for assessing the acoustic configuration and can be used to inform dereverberation algorithms. We describe a novel DRR estimation algorithm applicable where the signal was recorded with two or more microphones, such as mobile communications devices and laptops. The method uses a null-steered beamformer. In simulations the proposed method yields accurate DRR estimates to within ±4 dB across a wide variety of room sizes, reverberation times and source-receiver distances. It is also shown that the proposed method is more robust to background noise than a baseline approach. The best estimation accuracy is obtained in the region from -5 to 5 dB which is a relevant range for portable devices.