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Many individuals worldwide are at risk of hearing loss due to unsafe acoustical exposure and chronic listening experience using personal audio devices. Assistive hearing devices(AHD), such as hearing-aids(HAs) and cochlear-implants(CIs) are a common choice for the restoration and rehabilitation of the auditory function. Audio sound processors in CIs and HAs operate within limits, prescribed by audiologists, not only for acceptable sound perception but also for safety reasons. Signal processing(SP) engineers follow best design practices to ensure reliable performance and incorporate necessary safety checks within the design of SP strategies to ensure safety limits are never exceeded irrespective of acoustic environments. This paper proposes a comprehensive testing and evaluation paradigm to investigate the behavior of audio devices that addresses the safety concerns in diverse acoustic conditions. This is achieved by characterizing the performance of devices with large amounts of acoustic inputs and monitoring the output behavior. The CCi-MOBILE Research-Interface(RI) (used for CI/HA research) is used in this study as the testing paradigm. Factors such as pulse-width(PW), inter-phase gap(IPG) and a number of other parameters are estimated to evaluate the impact of AHDs on hearing comfort, subjective sound quality and characterize audio devices in terms of listening perception and biological safety.

Cochlear implants (CIs) and hearing aids (HAs) are advanced assistive hearing devices that perform sound processing to achieve acoustic to acoustic/electrical stimulation, thus enabling the prospects for hearing restoration and rehabilitation. Since commercial CIs/HAs are typically constrained by manufacturer design/production constraints, it is necessary for researchers to use research platforms (RPs) to advance algorithms and conduct investigational studies with CI/HA subjects. While previous CI/HA research platforms exist, no study has explored establishing a formal evaluation protocol for the operational safety and reliability of RPs. This study proposes a two-phase analysis and evaluation paradigm for RPs. In the acoustic phase 1 step, a signal processing acoustic space is explored in order to present a sampled set of audio input content to explore the safety of the resulting output electric/acoustic stimulation. In the parameter phase 2 step, the configurable space for realizable electrical stimulation pulses is determined, and overall stimulation reliability and safety are evaluated. The proposed protocol is applied and demonstrated using Costakis Cochlear Implant Mobile. Assessment protocol observations, results, and additional best practices for subsampling of the acoustic and parameter test spaces are discussed. The proposed analysis-evaluation protocol establishes a viable framework for assessing RP operational safety and reliability. Guidelines for adapting the proposed protocol to address variability in RP configuration due to experimental factors such as custom algorithms, stimulation techniques, and/or individualization are also considered.

Restoration of auditory function among hearing impaired individuals using Cochlear Implant (CI) technology has contributed significantly towards an improved quality of life. CI users experience greater challenges in recognizing speech effectively in noisy, reverberant, or time-varying diverse environments. Most CI research efforts focus on enhancing speech perception and environmental sound awareness has received little or no attention. This study focuses on a comparative analysis of normal hearing (NH) vs. CI environmental sound recognition using classifiers trained on learned sound representations using a CNN-based sound event model. Sounds experienced by CI listeners are recreated by auralizing electrical stimuli. CCi-MOBILE is used to generate electrical stimuli and Braecker Vocoder is used for auralization. Natural and auralized sound representations are then applied in order to develop NH and CI sound recognition models. Comparative assessment of environmental sound recognition is carried out by analyzing f1-scores and other performance characteristics. Benefits stemming from this research can help CI researchers improve sound recognition performance, develop novel sound processing algorithms, exclusively for environmental sounds, and identify optimal CI electrical stimulation characteristics to enhance sound perception. Among CI users, improvement in environmental sound awareness contributes to improved quality of life.

Internet of things (IoT) in healthcare, has effi-ciently accelerated medical monitoring and assessment through the real-time analysis of collected data. Hence, to support the hearing-impaired community with better calibrations to their clinical processors and hearing aids, a portable smart space interface - AURIS has been developed by the Cochlear Implant Processing Lab (CILab) at UT-Dallas. The proposed Auris interface periodically samples the acoustic space, and through a learn vs test phase, builds a Gaussian mixture model for each specific environmental locations. An effective connection is established by the Auris interface with the CRSS CCi-Mobile research platform through an android app to fine tune the con-figuration settings for cochlear implant (CI) or hearing aid (HA) users entering the room/location. Baseline objective evaluations have been performed in diverse naturalistic locations using 12 hours of audio data. The performance metrics is determined by a verified wireless communication, along with estimated acoustic environment knowledge and room classification at greater than 90% accuracy.

Naturalistic sounds carry rich information related to situational context or subject/system properties that contribute towards an acoustic awareness of the ambient environment. Sensorineural hearing loss reduces the functionality of the cochlea, auditory nerve, or central auditory pathways leading to degradation in auditory processing. Cochlear Implants (CIs) have been widely used to restore impaired auditory function and research advancements have focused on improving speech recognition and overall hearing-related quality of life. However, relatively few studies have investigated non-linguistic Sound Recognition (SR) among CI subjects. In this study, the recognition of sounds in CI users is assessed in a competing condition involving at least two non-linguistic sound sources. Furthermore, an end-to-end audio source separation neural network, SuDoRM-RF, with negative and permutation invariance training, is used for audio source recovery and comparatively assessed for potential identification improvement of non-linguistic sounds among CI users. Objective metrics such as classification accuracy, scale-invariant signal-to-distortion-ratio, and other audio quality related measures are used for assessment and subjective evaluation and compared against listener testing with CI subjects. The proposed study can model multi-source non-linguistic sound problems, the cocktail party effect and potentially provide an effective simulation for realistic listening test scenarios. [Study supported by NIH DC010494-01A.]

Hearing loss is an increasingly prevalent condition resulting from damage to the inner ear which causes a reduction in speech intelligibility. The societal need for assistive hearing devices has increased exponentially over the past two decades; however, actual human performance with such devices has only seen modest gains relative to advancements in digital signal processing (DSP) technology. A major challenge with clinical hearing technologies is the limited ability to run complex signal processing algorithms requiring high computation power. The CCi-MOBILE platform, developed at UT-Dallas, provides the research community with an open-source, flexible, easy-to-use, software-mediated, powerful computing research interface to conduct a wide variety of listening experiments. The platform supports cochlear implants (CIs) and hearing aids (HAs) independently, as well as bimodal hearing (i.e., a CI in one ear and HA in the contralateral ear). The platform is ideally suited to address hearing research for: both quiet and naturalistic noisy conditions, sound localization, and lateralization. The platform uses commercially available smartphone/tablet devices as portable sound processors and can provide bilateral electric and acoustic stimulation. The hardware components, firmware, and software suite are presented to demonstrate safety to the speech scientist and CI/HA user, highlight user-specificity, and outline various applications of the platform for research.

In this letter, we derive a new super Gaussian Joint Maximum a Posteriori based single microphone speech enhancement gain function. The developed Speech Enhancement method is implemented on a smartphone, and this arrangement functions as an assistive device to hearing aids. We introduce a tradeoff parameter in the derived gain function that allows the smartphone user to customize their listening preference, by controlling the amount of noise suppression and speech distortion in real-time based on their level of hearing comfort perceived in noisy real world acoustic environment. Objective quality and intelligibility measures show the effectiveness of the proposed method in comparison to benchmark techniques considered in this paper. Subjective results reflect the usefulness of the developed Speech Enhancement application in real-world noisy conditions at signal to noise ratio levels of 0 dB and 5 dB., Comment: 5 pages

Research interfaces and assistive hearing devices (RI/AHD) enable investigators to perform perceptual studies and develop effective sound processing strategies for cochlear implant (CI) and hearing aid (HA) devices. Several concerns exist regarding safe and reliable operation of RI/AHDs, for example, safety, reliability, possible hazards, accuracy, and their long-term consistency. Recently, CRSS-CILab at UTDallas has developed a mobile RI (CCi-MOBILE) for unilateral and bilateral HA/CI devices. In this study, a comprehensive testing and evaluation strategy is proposed to investigate the behavior of RI/AHDs that addresses safety concerns, reliability of hardware/firmware design, hearing-comfort, subjective sound-quality, and range of human-driven settings, as well as diverse types of acoustic exposure. Furthermore, a characteristic risk-hazard analysis is performed including diagnosis and categorization of the severity level of the associated safety concerns by probing pulse characteristics from each electrode, e.g., current-level, pulse-width, charge per phase, frame-period (for stimulation-rate), inter-phase gap (IPG), and several other parameters. The CCi-MOBILE RI is employed in this study to demonstrate the utility of the proposed testing and evaluation paradigm. The described practices could potentially serve as a blue-print to characterize future RI for AHDs and several other personalized listening devices based on safety, reliability, listening comfort/perception, and subjective sound quality.

The UT-Dallas Costakis Cochlear Implant Mobile (CCi-MOBILE) research platform enables experimental research with hearing-aid (HA) and cochlear-implant (CI) devices. The platform substitutes the clinical sound processor (CI/HA) with an Android smartphone/tablet or a PC as a computing hardware to run custom speech processing algorithms. The flexibility offered by such a setup enables researchers to provide custom-designed electric and acoustic stimuli (EAS) to CIs and HAs bilaterally in a time-synchronized manner. With this bimodal (electric + acoustic) capability, the platform can be used to undertake studies with individuals who either use a CI in one ear and a HA in the contralateral ear, or use hybrid implants in one or both ears. The CCi-MOBILE software suite includes a range of research applications which address different experimental needs. In the present work, a real-time VOCODER is incorporated in the CCi-MOBILE platform to facilitate research involving acoustic simulations of CIs. Two traditional flavors of VOCODER processing are implemented, namely noise-band and sine-wave vocoding. Flexibility is provided to modify processing parameters such as number of channels, frequency band-widths, filter attributes, etc. This flexibility combined with the ability to conduct long-term studies beyond the laboratory in diverse naturalistic acoustic environments will help advance research in psychoacoustics.