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International Symposium on Signals, Circuits and Systems (ISSCS)

作者： Grama, Lacrimioara Buhus, Elena Roxana Rusu, Corneliu Tech Univ Cluj Napoca Fac Elect Telecommun & Informat Technol Signal Proc Grp Cluj Napoca Romania

ISBN: (纸本)9781538606742

In this work we compare different classification algorithms applied on different number of features (linear predictive coding coefficients) in order to detect audio signals from wildlife areas. The final goal is to find the appropriate number of linear predictive coding coefficients to provide the desired accuracy for a certain framework. The experimental results prove that the best classifier is Logistic Model Trees regardless the number of features, having a constant classification accuracy greater than 95%. In the case of a reduced number of features, both Random Forest and Lazy IBk have good results;the classification accuracy is greater than 98%.

关键词： Classification algorithms Logistics linear predictive coding Feature extraction Birds Wildlife Vegetation

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LPCSE: Neural Speech Enhancement through linear predictive coding

LPCSE: Neural Speech Enhancement through Linear Predictive C...

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IEEE Global Communications Conference (GLOBECOM)

作者： Liu, Yang Tang, Na Chu, Xiaoli Yang, Yang Wang, Jun Univ Sheffield Sheffield S Yorkshire England Univ Chinese Acad Sci Beijing Peoples R China Terminus Grp Beijing Peoples R China UCL London England

ISBN: (纸本)9781665435406

The increasingly stringent requirement on quality-of-experience in 5G/B5G communication systems has led to the emerging neural speech enhancement techniques, which however have been developed in isolation from the existing expert-rule based models of speech pronunciation and distortion, such as the classic linear predictive coding (LPC) speech model because it is difficult to integrate the models with auto-differentiable machine learning frameworks. In this paper, to improve the efficiency of neural speech enhancement, we introduce an LPC-based speech enhancement (LPCSE) architecture, which leverages the strong inductive biases in the LPC speech model in conjunction with the expressive power of neural networks. Differentiable end-to-end learning is achieved in LPCSE via two novel blocks: a block that utilizes the expert rules to reduce the computational overhead when integrating the LPC speech model into neural networks, and a block that ensures the stability of the model and avoids exploding gradients in end-to-end training by mapping the linear prediction coefficients to the filter poles. The experimental results show that LPCSE successfully restores the formants of the speeches distorted by transmission loss, and outperforms two existing neural speech enhancement methods of comparable neural network sizes in terms of the Perceptual evaluation of speech quality (PESQ) and Short-Time Objective Intelligibility (STOI) on the LJ Speech corpus.

关键词： speech enhancement linear predictive coding neural networks formant distortion

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linear predictive coding of myoelectric signals

Linear predictive coding of myoelectric signals

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： E.S.G. Carotti J.C. De Martin D. Farina R. Merletti Dipartimento di Automatica e Informatica Politecnico di Milano Torino Italy IEIIT-CNR Politecnico di Milano Torino Italy Dipartimento di Elettronica Politecnico di Milano Torino Italy

Despite the great interest towards long term recordings of electromyographic (EMG) signals, which find applications, for example, in telemedicine, only a few studies have dealt with the compression of these signals. We propose a lossy coding technique for surface EMG signals. The technique is based on the linear predictive coding paradigm widely used for speech compression. The algorithm was tested on both simulated and experimental signals. Mean frequency, median frequency, variance, skewness and kurtosis of the EMG signals were preserved with an error less than 3% with respect to the original values for synthetic signals and experimental signals, reducing the bitrate from 24 kbit/s (12 kbit/s after downsampling) to 352 bit/s, with a compression factor of 97.1%. It was concluded that the linear predictive coding paradigm can be effectively used for high rate compression of surface EMG signals when preservation of only the power spectrum of the signal is of interest. This has applications in ergonomics and occupational medicine.

关键词： linear predictive coding Electromyography Muscles Speech coding Image coding Telemedicine Testing Frequency Occupational medicine Pulse modulation

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linear predictive coding of imagery for data compression applications

Linear predictive coding of imagery for data compression app...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： P. Poehler Junho Choi DBA Systems Inc. Melbourne FL USA

Extremely efficient compression algorithms can be devised for digital imagery data via an extension of the linear predictive coding methods utilized extensively in speech processing. When appropriately employed, these methods introduce minimal distortion. Such realizations can operate in real time, completely in the spatial domain, and are capable of reducing imagery storage requirements by an order of magnitude. This paper describes such an extension of linear predictive coding techniques for imagery data compression applications both theoretically and experimentally. This realization includes several heretofore uncombined and novel approaches to the imagery compression problem including: 2-D lattice filter prediction, adaptive quantization, and entropy coding. System implementation shows proof of feasibility and favorable performance in comparison with alternative transform techniques using the standard Minimum Mean Square Error [MMSE) fidelity criterion.

关键词： linear predictive coding Data compression Image storage Compression algorithms Digital images Speech processing Image coding Lattices Adaptive filters Quantization

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linear predictive coding of speech signals in a high ambient noise environment

Linear predictive coding of speech signals in a high ambient...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： H. Kobatake J. Inari S. Kakuta Faculty of Technology Tokyo University of Agriculture슠and슠Technology Koganei Tokyo Japan

Preservation of both the spectral distribution and the periodicity of speech signals are essential in speech processing. This paper describes a method of speech coding in a high ambient noise environment and shows that the spectral envelope of speech signal is a most reliable information when the noise reduction method proposed in this paper is used. Also reported in this paper comparisons of several pitch extraction methods with extensive experimental data, based on which a pitch extraction method suited for noisy speech signals is proposed.

关键词： linear predictive coding Speech enhancement Speech coding Working environment noise Speech processing Signal processing Wiener filter predictive models Speech synthesis Noise reduction

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linear predictive coding and Wavelet Decomposition for Robust Microarray Data Clustering

Linear Predictive Coding and Wavelet Decomposition for Robus...

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29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), vol.11

作者： Robert S. H. Istepanian Ala Sungoor Jean-Christophe Nebel Mobile Information and Network Technologies Research Centre (MINT) Kingston University London UK

Microarrays are powerful tools for simultaneous monitoring of the expression levels of large number of genes. Their analysis is usually achieved by using clustering techniques. Genomic signal processing is a new area of research that combines genomics with digital signal processing methodologies. In this paper, we present a comparative analysis of two genomic signal processing methods namely linear predictive coding and Discrete Wavelet Decomposition for robust microarray data clustering. Vector quantization is applied to the resultant coefficients to provide the clustering of the data samples. Both techniques were validated for standard data sets. Comparative analyses of the results indicate that these methods provide improved clustering accuracy compared to some conventional clustering techniques. Moreover, there classifiers don't require any prior training procedures

关键词： linear predictive coding Robustness Genomics Bioinformatics Digital signal processing Monitoring Signal analysis Signal processing Wavelet analysis Discrete wavelet transforms

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linear predictive coding systems

Linear predictive coding systems

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： T. Tremain National Security Agency Meade MD USA

This paper describes the real time implementation of a linear predictive coding algorithm that has been developed over the past five years. The algorithm chosen for the analyzer is a modification of the Covariance Method introduced by B. S. Atal [1],[2] of Bell Labs. The system for pitch extraction uses a minimum distance function correlation technique. A dynamic programming algorithm [3] is used for pitch smoothing and correction of isolated pitch errors. The synthesizer uses a transversal filter. Considerable time has been devoted to optimizing the running time and integer scaling of the different algorithms for real time implementation on a 16 bit mini-computer.

关键词： linear predictive coding Symmetric matrices Speech analysis Buffer storage predictive coding Analysis of variance Government Equations Signal processing Speech synthesis

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linear predictive coding for Enhanced Microarray Data Clustering

Linear Predictive Coding for Enhanced Microarray Data Cluste...

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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Robert S. H. Istepanian Ala Sungoor Jean-Christophe Nebel Mobile Information and Network Technologies Research Centre Kingston University Kingston-Upon-Thames London UK

Microarrays are powerful tools for simultaneous monitoring of the expression levels of large number of genes. Their analysis is usually achieved by using clustering techniques. In this paper, we present a new clustering method based on linear predictive coding to provide enhanced microarray data analysis. In this approach, spectral analysis of microarray data is performed to classify samples according to their distortion values. The technique was validated for a standard data set. Comparative analysis of the results indicates that this method provides improved clustering accuracy compared to some conventional clustering techniques. Moreover, our classifier does not require any prior training procedure.

关键词： linear predictive coding Gene expression Distortion measurement Genomics Bioinformatics Data analysis Spectral analysis Data mining Cardiovascular diseases Signal processing

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Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第Part1期80卷 000-000页

作者： Khodaei, Amin Shams, Parvaneh Sharifi, Hadi Mozaffari-Tazehkand, Behzad Univ Tabriz Fac Elect & Comp Engn 29 Bahman Blvd Tabriz Iran Istanbul Aydin Univ Comp Engn Dept Istanbul Turkiye

Corona disease has become one of the problems and challenges of humankind over the past two years. One of the problems that existed from the first days of this epidemic was clinical symptoms similar to other infectious vi-ruses such as colds and influenza. Therefore, diagnosis of this disease and its coping and treatment approaches is also been difficult. In this study, Attempts has been made to investigate the origin of this disease and the genetic structure of the virus leading to it. For this purpose, signal processing and linear predictive coding approaches were used which are widely used in data compression. A pattern recognition model was presented for the detection and separation of covid samples from the influenza virus case studies. This model, which was based on support vector machine classifier, was tested successfully on several datasets collected from different countries. The obtained results performed on all datasets by more than 98% accuracy. The proposed model, in addition to its good performance accuracy, can be a step forward in quantifying and digitizing medical big data information.

关键词： linear predictive coding Corona DNA Sequence Support vector machine Signal processing Machine learning

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Detection of Oscillatory Failures in Hydraulic Actuators of Aircraft using linear predictive coding and Signal Spectrum Analysis

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IFAC-PapersOnLine 2023年第2期56卷 360-365页

作者： Fawad Farooq Ashraf Surrayya Mobeen Hafiz Zeeshan Iqbal Khan Muhammad Farooq Haydar Jamshed Riaz Centers of Excellence in Science and Applied Technologies Islamabad Pakistan Department of Aeronautics & Astronautics Institute of Space Technology Islamabad Pakistan

This paper focuses on fault detection for oscillatory failures in hydraulic actuators of aircraft. These oscillatory failures, if not rectified in time, can cause severe loads on the airframe and eventually lead to structural damage. In this paper, a novel oscillatory failure case (OFC) detection algorithm is proposed which uses a nonlinear observer based on the mathematical model of the actuator in order to generate residual following a linear predictive coding analysis of the residual to detect oscillatory behavior. Finally, OFC decisions are made after the quantification of the residual in frequency domain. To illustrate the effectiveness of the proposed algorithm, results are presented using a high-fidelity industrial benchmark simulation. Furthermore, a comparative study is provided against an existing technique.

关键词： Oscillatory Failure Hydraulic Actuator Fault Detection linear predictive coding Signal Spectrum

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