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Digital processing of speech signals

Digital Coding of Waveforms

A survey on fall detection: Principles and approaches

Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for “good” reconstruction quality.

/pdf/compressed-sensing-for-real-time-energy-efficient-ecg-5bqmb6npzx.pdf

Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes

In open-set speaker identification systems a known phenomenon is that the false alarm (accept) error rate increases dramatically when increasing the number of registered speakers (models). In this paper, we demonstrate this phenomenon and suggest a solution using a new model-dependent score-normalization technique, called top-norm. The top-norm method was specifically developed to improve results of open-set speaker identification systems. Also, we suggest a score-normalization parameter adaptation technique. Experiments performed using speaker recognition corpora are described and demonstrate that the new method outperforms other normalization methods

/pdf/how-to-deal-with-multiple-targets-in-speaker-identification-4d15yh0con.pdf

How to Deal with Multiple-Targets in Speaker Identification Systems?

Study Objectives:Sound level meter is the gold standard approach for snoring evaluation. Using this approach, it was established that snoring intensity (in dB) is higher for men and is associated w...

Breathing and Snoring Sound Characteristics during Sleep in Adults.

Advances in Audio-Based Systems to Monitor Patient Adherence and Inhaler Drug Delivery

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that involves difficulties in social communication. Previous research has demonstrated that these difficulties are apparent in the way ASD children speak, indicating that it may be possible to estimate ASD severity using quantitative features of speech. Here, we extracted a variety of prosodic, acoustic, and conversational features from speech recordings of Hebrew speaking children who completed an Autism Diagnostic Observation Schedule (ADOS) assessment. Sixty features were extracted from the recordings of 72 children and 21 of the features were significantly correlated with the children’s ADOS scores. Positive correlations were found with pitch variability and Zero Crossing Rate (ZCR), while negative correlations were found with the speed and number of vocal responses to the clinician, and the overall number of vocalizations. Using these features, we built several Deep Neural Network (DNN) algorithms to estimate ADOS scores and compared their performance with Linear Regression and Support Vector Regression (SVR) models. We found that a Convolutional Neural Network (CNN) yielded the best results. This algorithm predicted ADOS scores with a mean RMSE of 4.65 and a mean correlation of 0.72 with the true ADOS scores when trained and tested on different sub-samples of the available data. Automated algorithms with the ability to predict ASD severity in a reliable and sensitive manner have the potential of revolutionizing early ASD identification, quantification of symptom severity, and assessment of treatment efficacy.

/pdf/estimating-autism-severity-in-young-children-from-speech-1zbj7i6704.pdf

Estimating Autism Severity in Young Children From Speech Signals Using a Deep Neural Network

The authors introduce a new ECG compression algorithm, and a new distortion measure. The distortion measure, called the Weighted Diagnostic Distortion (WDD), is based on comparing PQRST complex features (such as: RR interval, QT interval, ST elevation, etc.) of the original ECG signal and the reconstructed one. The compression algorithm is based on analysis by synthesis coding. It consists of a beat codebook, long and short term predictors, and an adaptive residual quantizer. The compression algorithm uses the WDD measure in order to encode, by means of analysis by synthesis, every beat of the original ECG signal. The compression algorithm has been applied to the MIT-BIH Arrhythmia Database. A rate of approximately 100 bits per second has been achieved with a very good quality (WDD below 4%, and PRD below 8%).

Yaniv Zigel

Papers

How to Deal with Multiple-Targets in Speaker Identification Systems?

Breathing and Snoring Sound Characteristics during Sleep in Adults.

Advances in Audio-Based Systems to Monitor Patient Adherence and Inhaler Drug Delivery

Estimating Autism Severity in Young Children From Speech Signals Using a Deep Neural Network

Analysis by synthesis ECG signal compression