Damage identification for beams in noisy conditions based on Teager energy operator-wavelet transform modal curvature

A time–frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions

Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen’s k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Due to the profound differences between acoustic characteristics of neutral and whispered speech, the performance of traditional automatic speech recognition (ASR) systems trained on neutral speech degrades significantly when whisper is applied. In order to deeply analyze this mismatched train/test situation and to develop an efficient way for whisper recognition, this study first analyzes acoustic characteristics of whispered speech, addresses the problems of whispered speech recognition in mismatched conditions, and then proposes a new robust cepstral features and preprocessing approach based on deep denoising autoencoder (DDAE) that enhance whisper recognition. The experimental results confirm that Teager-energy-based cepstral features, especially TECCs, are more robust and better whisper descriptors than traditional Mel-frequency cepstral coefficients (MFCC). Further detailed analysis of cepstral distances, distributions of cepstral coefficients, confusion matrices, and experiments with inverse filtering, prove that voicing in speech stimuli is the main cause of word misclassification in mismatched train/test scenarios. The new framework based on DDAE and TECC feature, significantly improves whisper recognition accuracy and outperforms traditional MFCC and GMM-HMM (Gaussian mixture density—Hidden Markov model) baseline, resulting in an absolute 31% improvement of whisper recognition accuracy. The achieved word recognition rate in neutral/whisper scenario is 92.81%.

Whispered Speech Recognition Using Deep Denoising Autoencoder and Inverse Filtering

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

The Teager Energy Operator is a nonlinear operator defined for both
continuous and discrete time signals. In this thesis we define
different versions and extensions of the one-dimensional operator, and
then look at the properties and positivity of the operator. Its
connection to Volterra systems is explained. The Teager Energy
Operator is used for demodulation of AM-FM signals, and we look at how
this can be done using the operator and nonlinear methods (Hilbert
transform, Prony's, Modified covariance, etc). As the Teager Energy
Operator is sensitive to noise, the multiband demodulation technique
is introduced to refine the estimates. The performance of linear and
nonlinear differentiators is investigated. An application to speech
analysis is shown, using a multiband method to locate formants in
speech. Finally, a performance comparison in different levels of noise
is done.

Eivind Kvedalen

Papers

Signal processing using the Teager Energy Operator and other nonlinear operators