Practical utility of a head-mounted gaze-directed beamforming system.

Assistive auditory devices that enhance signal-to-noise ratio must follow the user's changing attention; errors could lead to the desired source being suppressed as noise. A method for measuring the practical benefit of attention-following speech enhancement is described and used to show a benefit for gaze-directed beamforming over natural binaural hearing. First, participants watched a recorded video conference call between two people with six additional interfering voices in different directions. The directions of the target voices corresponded to the spatial layout of their video streams. A simulated beamformer was yoked to the participant's gaze direction using an eye tracker. For the control condition, all eight voices were spatially distributed in a simulation of unaided binaural hearing. Participants completed questionnaires on the content of the conversation, scoring twice as high in the questionnaires for the beamforming condition. Sentence-by-sentence intelligibility was then measured using new participants who viewed the same audiovisual stimulus for each isolated sentence. Participants recognized twice as many words in the beamforming condition. The results demonstrate the potential practical benefit of gaze-directed beamforming for hearing aids and illustrate how detailed intelligibility data can be retrieved from an experiment that involves behavioral engagement in an ongoing listening task.

Speech recognition with a hearing-aid processing scheme combining beamforming with mask-informed speech enhancement.

A signal processing approach combining beamforming with mask-informed speech enhancement was assessed by measuring sentence recognition in listeners with mild-to-moderate hearing impairment in adverse listening conditions that simulated the output of behind-the-ear hearing aids in a noisy classroom. Two types of beamforming were compared: binaural, with the two microphones of each aid treated as a single array, and bilateral, where independent left and right beamformers were derived. Binaural beamforming produces a narrower beam, maximising improvement in signal-to-noise ratio (SNR), but eliminates the spatial diversity that is preserved in bilateral beamforming. Each beamformer type was optimised for the true target position and implemented with and without additional speech enhancement in which spectral features extracted from the beamformer output were passed to a deep neural network trained to identify time-frequency regions dominated by target speech. Additional conditions comprising binaural beamforming combined with speech enhancement implemented using Wiener filtering or modulation-domain Kalman filtering were tested in normally-hearing (NH) listeners. Both beamformer types gave substantial improvements relative to no processing, with significantly greater benefit for binaural beamforming. Performance with additional mask-informed enhancement was poorer than with beamforming alone, for both beamformer types and both listener groups. In NH listeners the addition of mask-informed enhancement produced significantly poorer performance than both other forms of enhancement, neither of which differed from the beamformer alone. In summary, the additional improvement in SNR provided by binaural beamforming appeared to outweigh loss of spatial information, while speech understanding was not further improved by the mask-informed enhancement method implemented here.

Narrowband multi-source direction-of-arrival estimation in the spherical harmonic domain.

A conventional approach to wideband multi-source (MS) direction-of-arrival (DOA) estimation is to perform single source (SS) DOA estimation in time-frequency (TF) bins for which a SS assumption is valid. Such methods use the W-disjoint orthogonality (WDO) assumption due to the speech sparseness. As the number of sources increases, the chance of violating the WDO assumption increases. As shown in the challenging scenarios with multiple simultaneously active sources over a short period of time masking each other, it is possible for a strongly masked source (due to inconsistency of activity or quietness) to be rarely dominant in a TF bin. SS-based DOA estimators fail in the detection or accurate localization of masked sources in such scenarios. Two analytical approaches are proposed for narrowband DOA estimation based on the MS assumption in a bin in the spherical harmonic domain. In the first approach, eigenvalue decomposition is used to decompose a MS scenario into multiple SS scenarios, and a SS-based analytical DOA estimation is performed on each. The second approach analytically estimates two DOAs per bin assuming the presence of two active sources per bin. The evaluation validates the improvement to double accuracy and robustness to sensor noise compared to the baseline methods.

Time-Frequency Analysis and Parameterisation of Knee Sounds for Non-Invasive Detection of Osteoarthritis.

OBJECTIVE: In this work the potential of non-invasive detection of knee osteoarthritis is investigated using the sounds generated by the knee joint during walking. METHODS: The information contained in the time-frequency domain of these signals and its compressed representations is exploited and their discriminant properties are studied. Their efficacy for the task of normal vs abnormal signal classification is evaluated using a comprehensive experimental framework. Based on this, the impact of the feature extraction parameters on the classification performance is investigated using Classification and Regression Trees, Linear Discriminant Analysis and Support Vector Machine classifiers. RESULTS: It is shown that classification is successful with an area under the Receiver Operating Characteristic curve of 0.92. CONCLUSION: The analysis indicates improvements in classification performance when using non-uniform frequency scaling and identifies specific frequency bands that contain discriminative features. SIGNIFICANCE: Contrary to other studies that focus on sit-to-stand movements and knee flexion/extension, this study used knee sounds obtained during walking. The analysis of such signals leads to non-invasive detection of knee osteoarthritis with high accuracy and could potentially extend the range of available tools for the assessment of the disease as a more practical and cost effective method without requiring clinical setups.

Personalized signal-independent beamforming for binaural hearing aids.

The effect of personalized microphone array calibration on the performance of hearing aid beamformers under noisy reverberant conditions is studied. The study makes use of a new, publicly available, database containing acoustic transfer function measurements from 29 loudspeakers arranged on a sphere to a pair of behind-the-ear hearing aids in a listening room when worn by 27 males, 14 females, and 4 mannequins. Bilateral and binaural beamformers are designed using each participant's hearing aid head-related impulse responses (HAHRIRs). The performance of these personalized beamformers is compared to that of mismatched beamformers, where the HAHRIR used for the design does not belong to the individual for whom performance is measured. The case where the mismatched HAHRIR is that of a mannequin is of particular interest since it represents current practice in commercially available hearing aids. The benefit of personalized beamforming is assessed using an intrusive binaural speech intelligibility metric and in a matrix speech intelligibility test. For binaural beamforming, both measures demonstrate a statistically signficant (p < 0.05) benefit of personalization. The benefit varies substantially between individuals with some predicted to benefit by as much as 1.5 dB.

Spatial consistency for multiple source direction-of-arrival estimation and source counting.

A conventional approach to wideband multi-source (MS) direction-of-arrival (DOA) estimation is to perform single source (SS) DOA estimation in time-frequency (TF) bins for which a SS assumption is valid. The typical SS-validity confidence metrics analyse the validity of the SS assumption over a fixed-size TF region local to the TF bin. The performance of such methods degrades as the number of simultaneously active sources increases due to the associated decrease in the size of the TF regions where the SS assumption is valid. A SS-validity confidence metric is proposed that exploits a dynamic MS assumption over relatively larger TF regions. The proposed metric first clusters the initial DOA estimates (one per TF bin) and then uses the members' spatial consistency as well as its cluster's spread to weight each TF bin. Distance-based and density-based clustering are employed as two alternative approaches for clustering DOAs. A noise-robust density-based clustering is also used in an evolutionary framework to propose a method for source counting and source direction estimation. The evaluation results based on simulations and also with real recordings show that the proposed weighting strategy significantly improves the accuracy of source counting and MS DOA estimation compared to the state-of-the-art.

Robust and efficient Bayesian adaptive psychometric function estimation.

The efficient measurement of the threshold and slope of the psychometric function (PF) is an important objective in psychoacoustics. This paper proposes a procedure that combines a Bayesian estimate of the PF with either a look one-ahead or a look two-ahead method of selecting the next stimulus presentation. The procedure differs from previously proposed algorithms in two respects: (i) it does not require the range of possible PF parameters to be specified in advance and (ii) the sequence of probe signal-to-noise ratios optimizes the threshold and slope estimates at a performance level, ϕ, that can be chosen by the experimenter. Simulation results show that the proposed procedure is robust and that the estimates of both threshold and slope have a consistently low bias. Over a wide range of listener PF parameters, the root-mean-square errors after 50 trials were ∼1.2 dB in threshold and 0.14 in log-slope. It was found that the performance differences between the look one-ahead and look two-ahead methods were negligible and that an entropy-based criterion for selecting the next stimulus was preferred to a variance-based criterion.

Statistical analysis of the autoregressive modeling of reverberant speech.

Hands-free speech input is required in many modern telecommunication applications that employ autoregressive (AR) techniques such as linear predictive coding. When the hands-free input is obtained in enclosed reverberant spaces such as typical office rooms, the speech signal is distorted by the room transfer function. This paper utilizes theoretical results from statistical room acoustics to analyze the AR modeling of speech under these reverberant conditions. Three cases are considered: (i) AR coefficients calculated from a single observation; (ii) AR coefficients calculated jointly from an M-channel observation (M > 1); and (iii) AR coefficients calculated from the output of a delay-and sum beamformer. The statistical analysis, with supporting simulations, shows that the spatial expectation of the AR coefficients for cases (i) and (ii) are approximately equal to those from the original speech, while for case (iii) there is a discrepancy due to spatial correlation between the microphones which can be significant. It is subsequently demonstrated that at each individual source-microphone position (without spatial expectation), the M-channel AR coefficients from case (ii) provide the best approximation to the clean speech coefficients when microphones are closely spaced (<0.3m).