Effects of Coordinated Bilateral Hearing Aids and Auditory Training on Sound Localization

This thesis has two main objectives: 1) evaluating the benefits of the bilateral coordination of the hearing aid Digital Signal Processing (DSP) features by measuring and comparing the auditory performance with and without the activation of this coordination, and 2) evaluating the benefits of acclimatization and auditory training on such auditory performance and, determining whether receiving training in one aspect of auditory performance (sound localization) would generalize to an improvement in another aspect of auditory performance (speech intelligibility in noise), and to what extent. Two studies were performed. The first study evaluated the speech intelligibility in noise and horizontal sound localization abilities in HI listeners using hearing aids that apply bilateral coordination of WDRC. A significant improvement was noted in sound localization with bilateral coordination on when compared to off, while speech intelligibility in noise did not seem to be affected. The second study was an extension of the first study, with a suitable period for acclimatization provided and then the participants were divided into training and control groups. Only the training group received auditory training. The training group performance was significantly better than the control group performance in some conditions, in both the speech intelligibility and the localization tasks. The bilateral coordination did not have significant effects on the results of the second study. This work is among the early literature to investigate the impact of bilateral coordination in hearing aids on the users’ auditory performance. Also, this work is the first to demonstrate the effect of auditory training in sound localization on the speech intelligibility performance

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc

Audio for Virtual, Augmented and Mixed Realities: Proceedings of ICSA 2019 ; 5th International Conference on Spatial Audio ; September 26th to 28th, 2019, Ilmenau, Germany

The ICSA 2019 focuses on a multidisciplinary bringing together of developers, scientists, users, and content creators of and for spatial audio systems and services. A special focus is on audio for so-called virtual, augmented, and mixed realities. The fields of ICSA 2019 are: - Development and scientific investigation of technical systems and services for spatial audio recording, processing and reproduction / - Creation of content for reproduction via spatial audio systems and services / - Use and application of spatial audio systems and content presentation services / - Media impact of content and spatial audio systems and services from the point of view of media science. The ICSA 2019 is organized by VDT and TU Ilmenau with support of Fraunhofer Institute for Digital Media Technology IDMT

Mixture of beamformers for speech separation and extraction

In many audio applications, the signal of interest is corrupted by acoustic background noise, interference, and reverberation. The presence of these contaminations can significantly degrade the quality and intelligibility of the audio signal. This makes it important to develop signal processing methods that can separate the competing sources and extract a source of interest. The estimated signals may then be either directly listened to, transmitted, or further processed, giving rise to a wide range of applications such as hearing aids, noise-cancelling headphones, human-computer interaction, surveillance, and hands-free telephony. Many of the existing approaches to speech separation/extraction relied on beamforming techniques. These techniques approach the problem from a spatial point of view; a microphone array is used to form a spatial filter which can extract a signal from a specific direction and reduce the contamination of signals from other directions. However, when there are fewer microphones than sources (the underdetermined case), perfect attenuation of all interferers becomes impossible and only partial interference attenuation is possible. In this thesis, we present a framework which extends the use of beamforming techniques to underdetermined speech mixtures. We describe frequency domain non-linear mixture of beamformers that can extract a speech source from a known direction. Our approach models the data in each frequency bin via Gaussian mixture distributions, which can be learned using the expectation maximization algorithm. The model learning is performed using the observed mixture signals only, and no prior training is required. The signal estimator comprises of a set of minimum mean square error (MMSE), minimum variance distortionless response (MVDR), or minimum power distortionless response (MPDR) beamformers. In order to estimate the signal, all beamformers are concurrently applied to the observed signal, and the weighted sum of the beamformers’ outputs is used as the signal estimator, where the weights are the estimated posterior probabilities of the Gaussian mixture states. These weights are specific to each timefrequency point. The resulting non-linear beamformers do not need to know or estimate the number of sources, and can be applied to microphone arrays with two or more microphones with arbitrary array configuration. We test and evaluate the described methods on underdetermined speech mixtures. Experimental results for the non-linear beamformers in underdetermined mixtures with room reverberation confirm their capability to successfully extract speech sources

Perception of Reverberation in Domestic and Automotive Environments

nrpages: 227status: publishe

Space Time Exploration of Musical Instruments

Musical instruments are tools used to generate sounds for musical expression. Virtual Reality (VR) and Augmented Reality (AR) musical instruments create sounds that may be spatially disjointed from the instrument controls. Spatial audio processing can be used to position the Extended Reality (XR) musical instruments and their corresponding sounds in the same space. This dissertation investigates novel ways of combining spatial reverb models to improve the naturalness of XR musical instruments. Seven spatial reverb systems, combinations of a shoebox spatial reverb model, a raytracing spatial reverb model, and measured directional room impulse response convolution reverb, were compared in a pilot study. A novel hybrid system of synthetic early reflections and directional room impulse responses was preferred for naturalness when tested over headphones with three instruments created by the author: AR electric guitar, AR drumset, and VR Singing Kite. This research culminated in a concert, Spherical Sound Search, which showcased the preferred hybrid system, the three XR musical instruments, and four re-contextualized spatial audio effects (spatial looping, spatial delay, spatial feedback, and spatial compression). The three pieces in the concert explored different aspects of XR modalities and presented the novel system with spatial audio effects to a larger audience by rendering to an octophonic loudspeaker layout