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作者： Silva Júnior, Júlio Peixoto da Universidade de Lisboa

Epilepsy is a brain disorder characterized by recurrent epileptic seizures that affects approximately 50 million people worldwide, making it one of the most common neurological diseases. The electroencephalogram (EEG) is an electrophysiological monitoring method that records the electrical activity of the brain and is widely used in the detection and analysis of epileptic seizures. However, it is often difficult to identify subtle but critical changes in the EEG waveform through visual *** EEG signals are nonlinear and non-stationary in nature, which contribute to further complexities related to their manual interpretation and detection of normal and abnormal (interictal and ictal) activities. Therefore, there is a need for a computer aided diagnostic system to automatically identify the anormal activities. It was found that the use of the attribute vector with the MFCC coefficients presented the best performance among the extraction methods, obtaining a sensitivity of 97%, specificity of 98% and a mean of 99.8% in all *** this dissertation we propose a system to aid the patient diagnosis in specific, in which a comparison was made between four methods of extraction of aracteriststicas: Power Spectral Density, linear predictive coding, Mel-Frequency Cepstral Coefficients and covariance matrix. These extraction methods were combined in scenarios with three types of randomized pattern classifiers: Extreme Learning Machine, Random Kitchen Sinks and Minimal Learning Machine. And for the purpose of comparison was used the classifier Support Vector Machine. In the simulations performed, the proposed scenarios used files with about one hour (in some cases up to four hours), were used and the results pointed out that the random classifiers are dependent on the method of extraction of characteristics used. It was found that the use of the attribute vector with MFCC coefficients presented the best performance among the extraction methods, obtaining a sen

关键词： Teleinformática Diagnóstico por computador Epilepsia - Classificação Epilepsy Features extraction Power spectral density linear predictive coding Extreme learning machine Minimal learning machine Random kitchen sinks Suport vector machine Disse

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Comparison of FPGA and GPU Implementations of LPC Algorithm for Voice Processing

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2018年第2期11卷 188-193页

作者： Sayadi, Fatma E. Bahri, Haythem Chouchene, Marwa Atri, Mohamed Monastir Univ Fac Sci Lab Elect & Microelect E E Environm St Monastir 5019 Tunisia

Background: Many applications in voice processing have high inherent parallelism. Field programmable gate array (FPGA) has shown very high performance in spite of its low operational frequency by fully extracting the parallelism. Nevertheless, recent CPU and graphic processing unit (GPU) have also an inherent for high performance. Methods: In fact, it becomes possible to utilize the parallelism using multi-cores, which support improved single instruction multiple data (SIMD) instruction. Recent GPUs support a large number of cores, and have a potential for high performance in many applications. Our goals are at first to compare GPU and FPGA implementation of the linear prediction coding (LPC) algorithm, in order to understand the trade-off between the flexibility but relatively low speed of an FPGA and the high speed and fixed architecture of the GPU. Secondly, we try to apply various levels optimization from overlapping data transfers to fine-tuning operation sequences. Results: The experimental results highlight the relative strengths and limitations of the two systems. Conclusion: Our experiments show that, for several samples corresponding to several speeches coding, GPU manages speedups of up to 3x compared to the FPGA and around 35x compared to a sequential execution.

关键词： Compute unified device architecture (CUDA) FPGA GPU linear predictive coding optimization strategies shared memory

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A benchmark of heart sound classification systems based on sparse decompositions 14

A benchmark of heart sound classification systems based on s...

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14th International Symposium on Medical Information Processing and Analysis

作者： Ibarra-Hernandez, Roilhi F. Bertin, Nancy Alonso-Arevalo, Miguel A. Guillen-Ramirez, Hugo A. Ensenada Ctr Sci Res & Higher Educ CICESE Ensenada BC Mexico Univ Rennes CNRS IRISA INRIA Rennes France

ISBN: (数字)9781510626065

ISBN: (纸本)9781510626065

Background: Nowadays, cardiovascular diseases (CVD) remain the main cause of death worldwide. A heart sound signal or phonocardiogram (PCG) is the most simple, economical and non-invasive tool to detect CVDs. Advances in technology and signal processing allow the design of computer-aided systems for heart illnesses detection from PCG signals. Purpose: The paper proposes a pipeline and benchmark for binary heart sounds classification. The features extraction architecture is focused on the use of Matching Pursuit time-frequency decomposition using Gabor dictionaries and the linear predictive coding method of a residual. We compare seven classifiers with two different approaches: feature averaging and cycle averaging. Methods: We test our proposal on the PhysioNet/CinC challenge 2016 database, which comprises a wide variety of heart sounds recorded from patients with normal and different pathological heart conditions. We conduct a 10-fold stratified cross-validation method to evaluate the performance of different classification algorithms. The feature sets were also tested when using an oversampling method for balancing. Results: The benchmark identified systems showing a satisfying performance in terms of accuracy, sensitivity, and Matthews correlation coefficient. Results can be improved when using feature averaging and an oversampling strategy.

关键词： Heart sounds Phonocardiogram Matching Pursuit linear predictive coding Cross Validation test Oversampling SMOTE

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I Predict, Therefore I Am: Perturbed predictive coding Under Ketamine and in Schizophrenia

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BIOLOGICAL PSYCHIATRY 2017年第6期81卷 465-466页

作者： Corlett, Philip R. Yale Univ Dept Psychiat New Haven CT 06511 USA

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MyLipper : A Personalized System for Speech Reconstruction using Multi-View Visual Feeds 20

MyLipper : A Personalized System for Speech Reconstruction u...

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20th IEEE International Symposium on Multimedia (ISM)

作者： Kumar, Yaman Jain, Rohit Salik, Khwaja Mohd. Shah, Rajiv Ratn Yin, Yifang Zimmermann, Roger Adobe San Jose CA USA Univ Delhi Netaji Subhas Inst Technol MIDAS Lab New Delhi India Indraprastha Inst Informat Technol New Delhi India Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781538668573

Lipreading is the task of looking at, perceiving, and interpreting spoken symbols. It has a wide range of applications such as in surveillance, Internet telephony, speech reconstruction for silent movies and as an aid to a person with speech as well as hearing impairments. However, most of the work in lipreading literature has been limited to the classification of speech videos into text classes formed of phrases, words and sentences. Even this has been based on a highly constrained lexicon of words which, then subsequently translates to restriction on total number of classes (i.e, phrases, words and sentences) that are considered for the classification task. Recently, research has ventured into generating speech (audio) from silent video sequences. In spite of non-frontal views showing the potential of enhancing performance of speech reading and reconstruction systems, there have been no developments in using multiple camera feeds for the same. To this end, this paper presents a multi-view speech reading and reconstruction system. The major contribution of this paper is to present a model, namely MyLipper, which is a vocabulary and language agnostic and a real-time model that deals with a variety of poses of a speaker. The model leverages silent video feeds from multiple cameras recording a subject to generate intelligent speech for that speaker, thus being a personalized speech reconstruction model. It uses deep learning based STCNN+BiGRU architecture to achieve this goal. The results obtained using MyLipper show an improvement of over 20% in reconstructed speech's intelligibility (as measured by PESQ) using multiple views as compared to a single view visual feed. This confirms the importance of exploiting multiple views in building an efficient speech reconstruction system. The paper further shows the optimal placement of cameras which would lead to the maximum intelligibility of speech. Further, we demonstrate the reconstructed audios overlaid on the correspondi

关键词： Multi-layer neural network Signal reconstruction Speech synthesis linear predictive coding

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Online Machine Learning Experiments in HTML5 48

Online Machine Learning Experiments in HTML5

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48th IEEE Frontiers in Education Conference (FIE)

作者： Dixit, Abhinav Shanthamallu, Uday Shankar Spanias, Andreas Berisha, Visar Banavar, Mahesh ASU Sch ECEE SenSIP Ctr Tempe AZ 85287 USA Clarkson Univ Dept ECE Potsdam NY 13676 USA

ISBN: (纸本)9781538611746

This work in progress paper describes software that enables online machine learning experiments in an undergraduate DSP course. This software operates in HTML5 and embeds several digital signal processing functions. The software can process natural signals such as speech and can extract various features, for machine learning applications. For example in the case of speech processing, LPC coefficients and formant frequencies can be computed. In this paper, we present speech processing, feature extraction and clustering of features using the K-means machine learning algorithm. The primary objective is to provide a machine learning experience to undergraduate students. The functions and simulations described provide a user-friendly visualization of phoneme recognition tasks. These tasks make use of the Levinson-Durbin linear prediction and the K-means machine learning algorithms. The exercise was assigned as a class project in our undergraduate DSP class. The description of the exercise along with assessment results is described.

关键词： Machine Learning Speech recognition linear predictive coding Online labs

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Blood Volume Pulse Extraction for Non-Contact Heart Rate Measurement by Digital Camera Using Singular Value Decomposition and Burg Algorithm

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ENERGIES 2018年第5期11卷 1076页

作者： Tayibnapis, Iman Rahmansyah Yang, Yeon-Mo Lim, Ki Moo Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi 39253 South Korea Kumoh Natl Inst Technol Dept Elect Engn Gumi 39177 South Korea

Conventional photoplesthymograph (PPG) measurements for heart rate (HR) determination require direct contact between the patient and the PPG device sensor. When using the conventional method, it is possible for users to suffer undesirable skin irritation, discomfort and soreness. Thus, the development of non-contact PPG has been investigated with various technologies and methods. One of the technologies that able to measure PPG in a non-contact way and at low cost is using digital cameras such as webcams. Various filters have been implemented to do non-contact PPG using digital cameras. This paper proposes a non-contact PPG filter system utilizing singular value decomposition (SVD) and Burg's algorithm. The main role of SVD is for noise removal and as PPG signal extractor. As for the Burg algorithm, it was utilized for estimating the heart rate value from the filtered PPG signal. In this paper, we show and analyze an experiment for HR measurement using our method and a previous method that used independent component analysis (ICA). We compare and contrast both of them with HR measurements acquired by a commercial oximeter. The experiments were conducted at various distance between 30 similar to 110 cm and light intensities between 5 similar to 2000 lux. The estimated HR showed 2.25 bpm of mean error and 0.73 of Pearson correlation coefficient. The optimal distance between the mirror and user for HR measurement was 50 cm with medium light intensity, around 550 lux.

关键词： biomedical signal processing blind source separation linear predictive coding photoplethysmography

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Validation of Computer Models for Evaluating the Efficacy of Cognitive Stimulation Therapy

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WIRELESS PERSONAL COMMUNICATIONS 2017年第3期94卷 301-314页

作者： Pham, Tuan D. Univ Aizu Res Ctr Adv Informat Sci & Technol Aizu Res Cluster Med Engn & Informat Aizu Wakamatsu Fukushima 9658580 Japan

The notion of using computational methods for evaluating cognitive stimulation therapy (CST) based on the synchronized recording of photoplethysmographic (PPG) signals of care-givers and participants offers an objective and cost-effective analysis in health care to improve the patient's quality of life. While computer models are promising as a useful tool for such a purpose, a question of interest is how the model reliability, which is the degree to which an assessment tool produces stable and consistent results, can be established. This paper addresses this issue with the application of dynamic-time warping and resampling to measure the performance of two PPG features known as the largest Lyapunov exponent and linear predictive coding, which have been applied for studying the efficacy of CST. The potential success of this computerized evaluation can be a precursor to the development of a personalized e-therapy system that operates on mobile devices.

关键词： Cognitive stimulation therapy Cognitive decline Model performance assessment Therapeutic communication Dynamic-time warping Photoplethysmograph Largest Lyapunov exponent linear predictive coding

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Smelting condition identification for a fused magnesium furnace based on an acoustic signal

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JOURNAL OF MATERIALS PROCESSING TECHNOLOGY 2017年 244卷 231-239页

作者： Fu, You Wang, Ninghui Wang, Zhen Wang, Zhiqiang Ji, Bing Wang, Xiaochen Dalian Univ Technol Sch Elect Engn Dalian 116024 Peoples R China Dalian Univ Technol Sch Innovat & Entrepreneurship Dalian 116024 Peoples R China Univ Leicester Dept Engn Univ Rd Leicester LE1 7RH Leics England Environm Engn Assessment Ctr Liaoning Prov Shenyang 110032 Peoples R China

To promote energy efficiency during *** furnace smelting, four smelting states were introduced in the smelting stage: an unmelted state, semi-molten state, molten state, and overheating state. A smelting identification system to distinguish these smelting states was developed through the use of linear predictive coding and a principal component analysis algorithm. A new smelting condition identification system was obtained. Corresponding pilot productions were conducted to compare the differences between employing the method and not employing the method. All of the pilot production data showed that feeding raw materials over time during the overheating state and decreasing current injection in the molten state could reduce energy consumption as well as increase crystal purity. (C) 2016 Published by Elsevier B.V.

关键词： Acoustic signal Fused magnesium furnace linear predictive coding Principal component analysis

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Design and evaluation of a parametric model for cardiac sounds

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COMPUTERS IN BIOLOGY AND MEDICINE 2017年 89卷 170-180页

作者： Ibarra-Hernandez, Roilhi F. Alonso-Arevalo, Miguel A. Cruz-Gutierrez, Alejandro Licona-Chavez, Ana L. Villarreal-Reyes, Salvador Ctr Invest Cient & Educ Super Ensenada Div Fis Aplicada Dept Elect & Telecomunicac Carretera Ensenada Tijuana 3918 Ensenada 22860 Baja California Mexico Ctr Estudios Univ Xochicalco Fac Med Campus EnsenadaSan Francisco 1139 Ensenada 22830 Baja California Mexico

Heart sound analysis plays an important role in the auscultative diagnosis process to detect the presence of cardiovascular diseases. In this paper we propose a novel parametric heart sound model that accurately represents normal and pathological cardiac audio signals, also known as phonocardiograms (PCG). The proposed model considers that the PCG signal is formed by the sum of two parts: one of them is deterministic and the other one is stochastic. The first part contains most of the acoustic energy. This part is modeled by the Matching Pursuit (MP) algorithm, which performs an analysis-synthesis procedure to represent the PCG signal as a linear combination of elementary waveforms. The second part, also called residual, is obtained after subtracting the deterministic signal from the original heart sound recording and can be accurately represented as an autoregressive process using the linear predictive coding (LPC) technique. We evaluate the proposed heart sound model by performing subjective and objective tests using signals corresponding to different pathological cardiac sounds. The results of the objective evaluation show an average Percentage of Root-Mean-Square Difference of approximately 5% between the original heart sound and the reconstructed signal. For the subjective test we conducted a formal methodology for perceptual evaluation of audio quality with the assistance of medical experts. Statistical results of the subjective evaluation show that our model provides a highly accurate approximation of real heart sound signals. We are not aware of any previous heart sound model rigorously evaluated as our proposal.

关键词： Heart sounds modeling Biomedical signal processing Matching Pursuit Time-frequency dictionaries Time-frequency atoms linear predictive coding

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