Towards a cyber physical system for personalised and automatic OSA treatment

Obstructive sleep apnea (OSA) is a breathing disorder that takes place in the course of the sleep and is produced by a complete or a partial obstruction of the upper airway that manifests itself as frequent breathing stops and starts during the sleep. The real-time evaluation of whether or not a patient is undergoing OSA episode is a very important task in medicine in many scenarios, as for example for making instantaneous pressure adjustments that should take place when Automatic Positive Airway Pressure (APAP) devices are used during the treatment of OSA. In this paper the design of a possible Cyber Physical System (CPS) suited to real-time monitoring of OSA is described, and its software architecture and possible hardware sensing components are detailed. It should be emphasized here that this paper does not deal with a full CPS, rather with a software part of it under a set of assumptions on the environment. The paper also reports some preliminary experiments about the cognitive and learning capabilities of the designed CPS involving its use on a publicly available sleep apnea database

Multisensory processing for speech enhancement and magnitude-normalized spectra for speech modeling

Abstract In this paper, we tackle the problem of speech enhancement from two fronts: speech modeling and multisensory input. We present a new speech model based on statistics of magnitude-normalized complex spectra of speech signals. By performing magnitude normalization, we are able to get rid of huge intra-and inter-speaker variation in speech energy and to build a better speech model with a smaller number of Gaussian components. To deal with real-world problems with multiple noise sources, we propose to use multiple heterogeneous sensors, and in particular, we have developed microphone headsets that combine a conventional air microphone and a bone sensor. The bone sensor makes direct contact with the speaker's temple (area behind the ear), and captures the vibrations of the bones and skin during the process of vocalization. The signals captured by the bone microphone, though distorted, contain useful audio information, especially in the low frequency range, and more importantly, they are very robust to external noise sources (stationary or not). By fusing the bone channel signals with the air microphone signals, much improved speech signals have been obtained

Time-Domain Multi-modal Bone/air Conducted Speech Enhancement

Previous studies have proven that integrating video signals, as a complementary modality, can facilitate improved performance for speech enhancement (SE). However, video clips usually contain large amounts of data and pose a high cost in terms of computational resources and thus may complicate the SE system. As an alternative source, a bone-conducted speech signal has a moderate data size while manifesting speech-phoneme structures, and thus complements its air-conducted counterpart. In this study, we propose a novel multi-modal SE structure in the time domain that leverages bone- and air-conducted signals. In addition, we examine two ensemble-learning-based strategies, early fusion (EF) and late fusion (LF), to integrate the two types of speech signals, and adopt a deep learning-based fully convolutional network to conduct the enhancement. The experiment results on the Mandarin corpus indicate that this newly presented multi-modal (integrating bone- and air-conducted signals) SE structure significantly outperforms the single-source SE counterparts (with a bone- or air-conducted signal only) in various speech evaluation metrics. In addition, the adoption of an LF strategy other than an EF in this novel SE multi-modal structure achieves better results.Comment: multi-modal, bone/air-conducted signals, speech enhancement, fully convolutional networ

Acoustic Feedback Noise Cancellation in Hearing Aids Using Adaptive Filter

To enhance speech intelligibility for people with hearing loss, hearing aids will amplify speech using gains derived from evidence-based prescriptive methods, in addition to other advanced signal processing mechanisms. While the evidence supports the use of hearing aid signal processing for speech intelligibility, these signal processing adjustments can also be detrimental to hearing aid sound quality, with poor hearing aid sound quality cited as a barrier to device adoption. In general, an uncontrolled environment may contain degradation components like background noise, speech from other speakers etc. along with required speech components. In this paper, we implement adaptive filtering design for acoustic feedback noise cancellation in hearing aids. The adaptive filter architecture has been designed using normalized least mean square algorithm. By using adaptive filters both filter input coefficients are changeable during run-time and reduce noise in hearing aids. The proposed design is implemented in matlab and the simulations shows that the proposed architecture produces good quality of speech, accuracy, maintain stable steady state. The proposed design is validated with parameters like Noise Distortion, Perceptual Evaluation of Speech Quality, Signal to Noise Ratio, and Speech Distortion. The feedback canceller is implemented in MATLAB 9.4 simulink version release name of R2018a is used for validation with Echo Return Loss Enhancement (ERLE). The ERLE of the NMLS is reduced when the filter order is increases. Around 10% of the power spectrum density (PSD) is less when compared with existing designs

Acoustic Feedback Noise Cancellation in Hearing Aids Using Adaptive Filter

To enhance speech intelligibility for people with hearing loss, hearing aids will amplify speech using gains derived from evidence-based prescriptive methods, in addition to other advanced signal processing mechanisms. While the evidence supports the use of hearing aid signal processing for speech intelligibility, these signal processing adjustments can also be detrimental to hearing aid sound quality, with poor hearing aid sound quality cited as a barrier to device adoption. In general, an uncontrolled environment may contain degradation components like background noise, speech from other speakers etc. along with required speech components. In this paper, we implement adaptive filtering design for acoustic feedback noise cancellation in hearing aids. The adaptive filter architecture has been designed using normalized least mean square algorithm. By using adaptive filters both filter input coefficients are changeable during run-time and reduce noise in hearing aids. The proposed design is implemented in matlab and the simulations shows that the proposed architecture produces good quality of speech, accuracy, maintain stable steady state. The proposed design is validated with parameters like Noise Distortion, Perceptual Evaluation of Speech Quality, Signal to Noise Ratio, and Speech Distortion. The feedback canceller is implemented in MATLAB 9.4 simulink version release name of R2018a is used for validation with Echo Return Loss Enhancement (ERLE). The ERLE of the NMLS is reduced when the filter order is increases. Around 10% of the power spectrum density (PSD) is less when compared with existing designs

Communications Biophysics

Contains research objectives and reports on eight research projects split into three sections.National Institutes of Health (Grant 2 PO1 NS13126)National Institutes of Health (Grant 5 RO1 NS18682)National Institutes of Health (Grant 5 RO1 NS20322)National Institutes of Health (Grant 1 RO1 NS 20269)National Institutes of Health (Grant 5 T32 NS 07047)Symbion, Inc.National Institutes of Health (Grant 5 R01 NS10916)National Institutes of Health (Grant 1 RO NS 16917)National Science Foundation (Grant BNS83-19874)National Science Foundation (Grant BNS83-19887)National Institutes of Health (Grant 5 RO1 NS12846)National Institutes of Health (Grant 1 RO1 NS21322-01)National Institutes of Health (Grant 5 T32-NS07099-07)National Institutes of Health (Grant 1 RO1 NS14092-06)National Science Foundation (Grant BNS77-21751)National Institutes of Health (Grant 5 RO1 NS11080