Ambisonics Networks -- The Effect Of Radial Functions Regularization

Ambisonics, a popular format of spatial audio, is the spherical harmonic (SH) representation of the plane wave density function of a sound field. Many algorithms operate in the SH domain and utilize the Ambisonics as their input signal. The process of encoding Ambisonics from a spherical microphone array involves dividing by the radial functions, which may amplify noise at low frequencies. This can be overcome by regularization, with the downside of introducing errors to the Ambisonics encoding. This paper aims to investigate the impact of different ways of regularization on Deep Neural Network (DNN) training and performance. Ideally, these networks should be robust to the way of regularization. Simulated data of a single speaker in a room and experimental data from the LOCATA challenge were used to evaluate this robustness on an example algorithm of speaker localization based on the direct-path dominance (DPD) test. Results show that performance may be sensitive to the way of regularization, and an informed approach is proposed and investigated, highlighting the importance of regularization information.

Ambisonics Encoding For Arbitrary Microphone Arrays Incorporating Residual Channels For Binaural Reproduction

In the rapidly evolving fields of virtual and augmented reality, accurate spatial audio capture and reproduction are essential. For these applications, Ambisonics has emerged as a standard format. However, existing methods for encoding Ambisonics signals from arbitrary microphone arrays face challenges, such as errors due to the irregular array configurations and limited spatial resolution resulting from a typically small number of microphones. To address these limitations and challenges, a mathematical framework for studying Ambisonics encoding is presented, highlighting the importance of incorporating the full steering function, and providing a novel measure for predicting the accuracy of encoding each Ambisonics channel from the steering functions alone. Furthermore, novel residual channels are formulated supplementing the Ambisonics channels. A simulation study for several array configurations demonstrates a reduction in binaural error for this approach.

Theoretical Framework for the Optimization of Microphone Array Configuration for Humanoid Robot Audition

An important aspect of a humanoid robot is audition. Previous work has presented robot systems capable of sound localization and source segregation based on microphone arrays with various configurations. However, no theoretical framework for the design of these arrays has been presented. In the current paper, a design framework is proposed based on a novel array quality measure. The measure is based on the effective rank of a matrix composed of the generalized head related transfer functions (GHRTFs) that account for microphone positions other than the ears. The measure is shown to be theoretically related to standard array performance measures such as beamforming robustness and DOA estimation accuracy. Then, the measure is applied to produce sample designs of microphone arrays. Their performance is investigated numerically, verifying the advantages of array design based on the proposed theoretical framework.

Direction of Arrival Estimation Using Microphone Array Processing for Moving Humanoid Robots

The auditory system of humanoid robots has gained increased attention in recent years. This system typically acquires the surrounding sound field by means of a microphone array. Signals acquired by the array are then processed using various methods. One of the widely applied methods is direction of arrival estimation. The conventional direction of arrival estimation methods assume that the array is fixed at a given position during the estimation. However, this is not necessarily true for an array installed on a moving humanoid robot. The array motion, if not accounted for appropriately, can introduce a significant error in the estimated direction of arrival. The current paper presents a signal model that takes the motion into account. Based on this model, two processing methods are proposed. The first one compensates for the motion of the robot. The second method is applicable to periodic signals and utilizes the motion in order to enhance the performance to a level beyond that of a stationary array. Numerical simulations and an experimental study are provided, demonstrating that the motion compensation method almost eliminates the motion-related error. It is also demonstrated that by using the motion-based enhancement method it is possible to improve the direction of arrival estimation performance, as compared to that obtained when using a stationary array.

Blind Localization of Room Reflections with Application to Spatial Audio

Blind estimation of early room reflections, without knowledge of the room impulse response, holds substantial value. The FF-PHALCOR (Frequency Focusing PHase ALigned CORrelation), method was recently developed for this objective, extending the original PHALCOR method from spherical to arbitrary arrays. However, previous studies only compared the two methods under limited conditions without presenting a comprehensive performance analysis. This study presents an advance by evaluating the performance of the algorithm in a wider range of conditions. Additionally, performance in terms of perception is investigated through a listening test. This test involves synthesizing room impulse responses from known room acoustics parameters and replacing the early reflections with the estimated ones. The importance of the estimated reflections for spatial perception is demonstrated through this test.

Subspace Hybrid MVDR Beamforming for Augmented Hearing

Signal-dependent beamformers are advantageous over signal-independent beamformers when the acoustic scenario - be it real-world or simulated - is straightforward in terms of the number of sound sources, the ambient sound field and their dynamics. However, in the context of augmented reality audio using head-worn microphone arrays, the acoustic scenarios encountered are often far from straightforward. The design of robust, high-performance, adaptive beamformers for such scenarios is an on-going challenge. This is due to the violation of the typically required assumptions on the noise field caused by, for example, rapid variations resulting from complex acoustic environments, and/or rotations of the listener's head. This work proposes a multi-channel speech enhancement algorithm which utilises the adaptability of signal-dependent beamformers while still benefiting from the computational efficiency and robust performance of signal-independent super-directive beamformers. The algorithm has two stages. (i) The first stage is a hybrid beamformer based on a dictionary of weights corresponding to a set of noise field models. (ii) The second stage is a wide-band subspace post-filter to remove any artifacts resulting from (i). The algorithm is evaluated using both real-world recordings and simulations of a cocktail-party scenario. Noise suppression, intelligibility and speech quality results show a significant performance improvement by the proposed algorithm compared to the baseline super-directive beamformer. A data-driven implementation of the noise field dictionary is shown to provide more noise suppression, and similar speech intelligibility and quality, compared to a parametric dictionary.

Performance Analysis Of Binaural Signal Matching (BSM) in the Time-Frequency Domain

The capture and reproduction of spatial audio is becoming increasingly popular, with the mushrooming of applications in teleconferencing, entertainment and virtual reality. Many binaural reproduction methods have been developed and studied extensively for spherical and other specially designed arrays. However, the recent increased popularity of wearable and mobile arrays requires the development of binaural reproduction methods for these arrays. One such method is binaural signal matching (BSM). However, to date this method has only been investigated with fixed matched filters designed for long audio recordings. With the aim of making the BSM method more adaptive to dynamic environments, this paper analyzes BSM with a parameterized sound-field in the time-frequency domain. The paper presents results of implementing the BSM method on a sound-field that was decomposed into its direct and reverberant components, and compares this implementation with the BSM computed for the entire sound-field, to compare performance for binaural reproduction of reverberant speech in a simulated environment.

Subspace Hybrid Beamforming for Head-worn Microphone Arrays

A two-stage multi-channel speech enhancement method is proposed which consists of a novel adaptive beamformer, Hybrid Minimum Variance Distortionless Response (MVDR), Isotropic-MVDR (Iso), and a novel multi-channel spectral Principal Components Analysis (PCA) denoising. In the first stage, the Hybrid-MVDR performs multiple MVDRs using a dictionary of pre-defined noise field models and picks the minimum-power outcome, which benefits from the robustness of signal-independent beamforming and the performance of adaptive beamforming. In the second stage, the outcomes of Hybrid and Iso are jointly used in a two-channel PCA-based denoising to remove the 'musical noise' produced by Hybrid beamformer. On a dataset of real 'cocktail-party' recordings with head-worn array, the proposed method outperforms the baseline superdirective beamformer in noise suppression (fwSegSNR, SDR, SIR, SAR) and speech intelligibility (STOI) with similar speech quality (PESQ) improvement.

EasyCom: An Augmented Reality Dataset to Support Algorithms for Easy Communication in Noisy Environments

Augmented Reality (AR) as a platform has the potential to facilitate the reduction of the cocktail party effect. Future AR headsets could potentially leverage information from an array of sensors spanning many different modalities. Training and testing signal processing and machine learning algorithms on tasks such as beam-forming and speech enhancement require high quality representative data. To the best of the author's knowledge, as of publication there are no available datasets that contain synchronized egocentric multi-channel audio and video with dynamic movement and conversations in a noisy environment. In this work, we describe, evaluate and release a dataset that contains over 5 hours of multi-modal data useful for training and testing algorithms for the application of improving conversations for an AR glasses wearer. We provide speech intelligibility, quality and signal-to-noise ratio improvement results for a baseline method and show improvements across all tested metrics. The dataset we are releasing contains AR glasses egocentric multi-channel microphone array audio, wide field-of-view RGB video, speech source pose, headset microphone audio, annotated voice activity, speech transcriptions, head bounding boxes, target of speech and source identification labels. We have created and are releasing this dataset to facilitate research in multi-modal AR solutions to the cocktail party problem.