Using blind source separation techniques to improve speech recognition in bilateral cochlear implant patients

This is the published version, also available here: http://dx.doi.org/10.1121/1.2839887.Bilateral cochlear implants seek to restore the advantages of binaural hearing by improving access to binaural cues. Bilateral implant users are currently fitted with two processors, one in each ear, operating independent of one another. In this work, a different approach to bilateral processing is explored based on blind source separation (BSS) by utilizing two implants driven by a single processor. Sentences corrupted by interfering speech or speech-shaped noise are presented to bilateral cochlear implant users at 0dB signal-to-noise ratio in order to evaluate the performance of the proposed BSS method. Subjects are tested in both anechoic and reverberant settings, wherein the target and masker signals are spatially separated. Results indicate substantial improvements in performance in both anechoic and reverberant settings over the subjects’ daily strategies for both masker conditions and at various locations of the masker. It is speculated that such improvements are due to the fact that the proposed BSS algorithm capitalizes on the variations of interaural level differences and interaural time delays present in the mixtures of the signals received by the two microphones, and exploits that information to spatially separate the target from the masker signals

Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination

An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem of a changing environment where the adaptive filter operates. This proposed model is called Fuzzy Counterpropagation Network (Fuzzy CPN). It has fast learning capability and self-growing structure. This model is applied to non-linear function approximation, chaotic time series prediction and background noise elimination

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Speech Signal Enhancement in Cocktail Party Scenarios by Deep Learning based Virtual Sensing of Head-Mounted Microphones

The cocktail party effect refers to the human sense of hearing’s ability to pay attention to a single conversation while filtering out all other background noise. To mimic this human hearing ability for people with hearing loss, scientists integrate beamforming algorithms into the signal processing path of hearing aids or implants’ audio processors. Although these algorithms’ performance strongly depends on the number and spatial arrangement of the microphones, most devices are equipped with a small number of microphones mounted close to each other on the audio processor housing. We measured and evaluated the impact of the number and spatial arrangement of hearing aid or head-mounted microphones on the performance of the established Minimum Variance Distortionless Response beamformer in cocktail party scenarios. The measurements revealed that the optimal microphone placement exploits monaural cues (pinna-effect), is close to the target signal, and creates a large distance spread due to its spatial arrangement. However, this microphone placement is impractical for hearing aid or implant users, as it includes microphone positions such as on the forehead. To overcome microphones’ placement at impractical positions, we propose a deep virtual sensing estimation of the corresponding audio signals. The results of objective measures and a subjective listening test with 20 participants showed that the virtually sensed microphone signals significantly improved the speech quality, especially in cocktail party scenarios with low signal-to-noise ratios. Subjective speech quality was assessed using a 3-alternative forced choice procedure to determine which of the presented speech mixtures was most pleasant to understand. Hearing aid and cochlear implant (CI) users might benefit from the presented approach using virtually sensed microphone signals, especially in noisy environments

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Virtual Reality as Navigation Tool: Creating Interactive Environments For Individuals With Visual Impairments

Research into the creation of assistive technologies is increasingly incorporating the use of virtual reality experiments. One area of application is as an orientation and mobility assistance tool for people with visual impairments. Some of the challenges are developing useful knowledge of the user’s surroundings and effectively conveying that information to the user. This thesis examines the feasibility of using virtual environments conveyed via auditory feedback as part of an autonomous mobility assistance system. Two separate experiments were conducted to study key aspects of a potential system: navigation assistance and map generation. The results of this research include mesh models that were fitted to the walk pathways of an environment, and collected data that provide insights on the viability of virtual reality based guidance systems