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A Novel Two-Stage Compression Scheme Combining Polar coding and linear prediction coding for Fronthaul Links in Cloud-RAN

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IEICE TRANSACTIONS ON COMMUNICATIONS 2017年第5期E100B卷 691-701页

作者： Yang, Fangliao Niu, Kai Dong, Chao Tian, Baoyu Liu, Zhihui Beijing Univ Posts & Telecommun Minist Educ Key Lab Universal Wireless Commun Beijing 100876 Peoples R China

The transmission on fronthaul links in the cloud radio access network has become a bottleneck with the increasing data rate. In this paper, we propose a novel two-stage compression scheme for fronthaul links. In the first stage, the commonly used techniques like cyclic prefix stripping and sampling rate adaptation are implemented. In the second stage, a structure called linear prediction coding with decision threshold (LPC-DT) is proposed to remove the redundancies of signal. Considering that the linear prediction outputs have large dynamic range, a two-piecewise quantization with optimized decision threshold is applied to enhance the quantization performance. In order to further lower the transmission rate, a multi-level successive structure of lossless polar source coding is proposed to compress the quantization output with low encoding and decoding complexity. Simulation results demonstrate that the proposed scheme with LPC-DT and LPSC offers not only significantly better compression ratios but also more flexibility in bandwidth settings compared with traditional ones.

关键词： Cloud-RAN data compression fronthaul linear prediction coding polar coding

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3D model retrieval based on linear prediction coding in cylindrical and spherical projections using SVM-OSS

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第4期74卷 1511-1535页

作者： Mehrdad, Vahid Ebrahimnezhad, Hossein Sahand Univ Technol Fac Elect Engn Comp Vision Res lab Tabriz Iran

This paper presents a 3D model descriptor based on the linear prediction coding (LPC) coefficients to retrieve 3D objects. In this method, the 3D object is projected on the lateral surface of a cylinder parallel to one of its principal axes and centered at the centroid of the object. To improve efficiency, cylindrical projection is performed on three cylinders parallel to principal axes. Besides, the object is projected on a sphere centered at the centroid of the 3D object. The surface of object is converted to two-dimensional shapes as a result of performing the projections which preserves the geometric features of the object. Then, LPC coefficients are extracted from the two-dimensional projected shapes. These coefficients estimate the parameters of correlated signal, efficiently. Since the cylindrical and spherical projections of the 3D model are correlated surfaces, LPC coefficients describe the surfaces as well. The rotation normalization is performed employing the principal component analysis. Furthermore, the retrieval performance is enhanced employing SVM-OSS similarity measure which efficiently compares two model feature vectors. We implement an experimental comparison on PSB database of 3D models in order to demonstrate the performance of the proposed descriptor. Experimental results show the effectiveness of the proposed descriptor compared to current methods.

关键词： 3D model retrieval linear prediction coding Shape descriptor Cylindrical and spherical projections

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Text-independent speaker identification system using discrete wavelet transform with linear prediction coding

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Journal of Umm Al-Qura University for Engineering and Architecture 2024年第2期15卷 112-119页

作者： Alrusaini, Othman Daqrouq, Khaled Department of Engineering and Applied Sciences Applied College Umm Al-Qura University Makkah 24382 Saudi Arabia Department of Electrical and Computer Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia

One of the key problems of the modern day is the presentation of an identity verification system that can perform sufficient accuracy in identity verification, is resilient to assaults and noises, and can be recorded in the simplest possible method. In this study, a new speaker feature extraction which based on discrete wavelet transform (DWT) and linear prediction coding (LPC) algorithm (WLPCA) are investigated. This paper's primary objective is to evidence the performance of the new method for speaker identification by a Gaussian mixture model (GMM). The proposed method improves the recognition rate over the Mel-frequency cepstral coefficient (MFCC). Experimental evaluation of the process performance is performed on two speech databases;our recorded database and the publicly available TIMIT database. We show that the speech features derived by the newly proposed method are more suitable for GMM (91.53%), in terms of the time-consumed, by requiring less Gaussian mixtures than MFCC (85.77%). For testing the presented method in a noisy environment, Additive white Gaussian noise (AWGN) was added to the TIMIT database, where a slight improvement in the performance of the presented method (60.02%) over the MFCC (59.89%) was observed. © The Author(s) 2024.

关键词： Gaussian mixture model linear prediction coding Speech Text-independent Wavelet

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The Delineation of Aortic Valve Opening Point and Estimation of Heart Rate Variability from Seismocardiogram signal using linear prediction coding Technique 19

The Delineation of Aortic Valve Opening Point and Estimation...

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19th IEEE-India-Council International Conference (INDICON)

作者： Singh, Yengkhom Omesh Sahoo, Sibasis Sharma, L. N. Dandapat, Samarendra Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati Assam India

ISBN: (纸本)9781665473507

Our study presents linear prediction coding (LPC) technique-based AO peak detection framework for seismo-cardiogram (SCG) signal and heart rate (HR) estimation. In the proposed method, residual signal is estimated by inverse filtering of SCG signal. This residual signal emphasizes the systole and diastole profiles of the SCG signal. The AO peaks of the systole profiles are detected using Hilbert transform and first-order gaussian difference (FOGD) filter. The proposed method is tested on the CEBS database from the PhysioNet archive. The best overall performance for detecting AO peaks is found to be precision of 96.23%, sensitivity of 97.78%, and accuracy of 94.17%. For robustness of the proposed method, Monte Carlo simulation is performed for the noise stress test by adding additive white gaussian noise (AWGN) to the SCG signal. The proposed method can achieve good performance under this simulation. Further, the heart rate variability (HRV) is estimated using our proposed method and compared with the HRV evaluated from the reference ECG signal. The proposed method can achieve performance close to the reference value.

关键词： Seismocardiogram linear prediction coding residual signal AO peaks

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Text-dependent speaker verification using discrete wavelet transform based on linear prediction coding

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第PartB期86卷

作者： Ketabi, Sina Rashidi, Saeid Fallah, Ali Amirkabir Univ Technol Dept Biomed Engn Hafez Ave Tehran Iran Islamic Azad Univ Dept Med Sci & Technol Sci & Res Branch Daneshgah BlvdSimon Bulivar Blvd Tehran Iran

Presenting a system for verifying the identity of people so that the data could be recorded in the simplest possible way, performing identity verification with acceptable accuracy, and being robust to attacks and noises is one of some important issues in today's era. This paper presents a text-dependent speaker verification system in which features are extracted from the data using a method called discrete wavelet transform based on linear prediction coding. In addition to the conventional classification in these systems, a new method for classification in which one binary classification will perform for all speakers of the dataset can also be done, which will be introduced in this research. Then the performance of the designed system on ASVspoof 2015, ASVspoof 2017, ASVspoof 2019, AudioMNIST, and TIMIT datasets will be evaluated using accuracy, precision, sensitivity, specificity, F1-score, and equal error rate (EER) metrics. It will be shown that the EER for the case where classification is done once for each speaker is 0.14 & PLUSMN;0.83% for the ASVspoof 2017 dataset and the accuracy and EER for the case where classification is done once for all speakers is 100 & PLUSMN;0.00% and 0.00 & PLUSMN;0.00% for all datasets.

关键词： Text-dependent Speaker verification Discrete wavelet transform linear prediction coding Classification

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SHORT UTTERANCE RECOGNITION USING A NETWORK WITH MINIMUM TRAINING

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NEURAL NETWORKS 1991年第6期4卷 711-722页

作者： TOM, MD TENORIO, MF Purdue University USA

A feedforward network is used to recognize short, digitized, isolated utterances. A high, multispeaker recognition rate is achieved with a small vocabulary with a single training utterance. This approach makes use of the pattern recognition property of the network architecture to classify different temporal patterns in the multidimensional feature space. The network recognizes the utterances without the need of segmentation, phoneme identification, or time alignment. We train the network with four words spoken by one single speaker. The network is then able to recognize 20 tokens spoken by 5 other speakers. We repeat the above training and testing procedure using a different speaker's utterances for training each time. The overall accuracy is 97.5%. We compare this approach to the traditional dynamic programming (DP) approach, and find that DP with slope constrains of 0 and 1 achieve 98.5% and 85% accuracies respectively. Finally we validate out statistics by training and testing the network of a four-word subset of the Texas Instruments (TI) isolated word database. The accuracy with this vocabulary exceeds 96%. By doubling the size of the training set, the accuracy is raised to 98%. Using a suitable threshold, we are able to raise the accuracy of one network from 87% to 98.5%. Thresholding applied to all networks would then raise the overall accuracy to well over 99%. This technique is especially promising because of the low overhead and computational requirements, which make it suitable for a low cost, portable, command recognition type of application.

关键词： SHORT UTTERANCE RECOGNITION MINIMUM TRAINING MULTISPEAKER TESTING SPATIOTEMPORAL PATTERNS linear prediction coding GENERALIZATION

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Two-Sided LPC-Based Speckle Noise Removal for Laser Speech Detection Systems

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2021年第6期E104D卷 850-862页

作者： Wang, Yahui Zhang, Wenxi Kong, Xinxin Wang, Yongbiao Zhang, Hongxin Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing Peoples R China Chinese Acad Sci Key Lab Computat Opt Imaging Technol Beijing Peoples R China

Laser speech detection uses a non-contact Laser Doppler Vibrometry (LDV)-based acoustic sensor to obtain speech signals by precisely measuring voice-generated surface vibrations. Over long distances, however, the detected signal is very weak and full of speckle noise. To enhance the quality and intelligibility of the detected signal, we designed a two-sided linear prediction coding (LPC)-based locator and interpolator to detect and replace speckle noise. We first studied the characteristics of speckle noise in detected signals and developed a binary-state statistical model for speckle noise generation. A two-sided LPC-based locator was then designed to locate the polluted samples, composed of an inverse decorrelator, nonlinear filter and threshold estimator. This greatly improves the detectability of speckle noise and avoids false/missed detection by improving the noise-to-signal-ratio (NSR). Finally, samples from both sides of the speckle noise were used to estimate the parameters of the interpolator and to code samples for replacing the polluted samples. Real-world speckle noise removal experiments and simulation-based comparative experiments were conducted and the results show that the proposed method is better able to locate speckle noise in laser detected speech and highly effective at replacing it.

关键词： speckle noise removal linear prediction coding speech enhancement laser speech detection

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"Staying Out" Rather than "Cracking in": Asymmetric Membrane Insertion of 12:0 Lysophosphocholine

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LANGMUIR 2016年第44期32卷 11655-11663页

作者： Fan, Helen Y. Das, Dew Heerklotz, Heiko Univ Toronto Leslie Dan Fac Pharm Toronto ON Canada Univ Freiburg Inst Pharmaceut Sci Freiburg Germany BIOSS Ctr Biol Signalling Studies Freiburg Germany

Interactions between detergents and model membranes are well described by the three-stage model: saturation and solubilization boundaries divide bilayer-only, bilayer micelle coexistence, and micelle-only ranges. An underlying assumption of the model is the equilibration of detergent between the two membrane leaflets. However, many detergents partition asymmetrically at room temperature due to slow flip-flop, such as sodium dodecyl sulfate (SDS) and lysolipids. In this work, we use isothermal titration calorimetry (ITC) and dynamic light scattering (DLS) to investigate the solubilization of unilamellar POPC vesicles by 12:0 lysophosphocholine (12:0 LPC). Flip-flop of 12:0 LPC occurs beyond the time scale of our experiments, which establish a characteristic nonequilibrated state with asymmetric distribution: 12:0 LPC partitions primarily into the outer leaflet. Increasing asymmetry stress in the membrane does not lead to membrane failure, i.e., "cracking in" as seen for alkyl maltosides and other surfactants;instead, it reduces further membrane insertion which leads to the "staying out" of 12:0 LPC in solution. At above the critical micellar concentration of 12:0 LPC in the presence of the membrane, micelles persist and accommodate further LPC but take up lipid from vesicles only very slowly. Ultimately, solubilization proceeds via the micellar mechanism (Kragh-Hansen et al., 1995). With a combination of demicellization and solubilization experiments, we quantify the molar ratio partition coefficient (0.6 +/- 0.1 mM(-1)) and enthalpy of partitioning (6.1 +/- 0.3 ***(-1)) and estimate the maximum detergent/lipid ratio reached in the outer leaflet (<0.13). Despite the inapplicability of the three-stage model to 12:0 LPC at room temperature, we are able to extract quantitative information from ITC solubilization experiments and DLS that are important for the understanding of asymmetry-dependent processes such as endocytosis and the gating of mechanosensitive channels

关键词： CHEMICAL CRACKING PCSK7 gene Room temperature Surface-Active Agents cracks time scales Detergents dodecyl sulfate sodium Environmental Temperature membrane partitions solubilization linear prediction coding

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Motor imagery task classification using transformation based features

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2017年第0期33卷 213-219页

作者： Khorshidtalab, Aida Salami, Momoh J. E. Akmeliawati, Rini Int Islamic Univ Malaysia Intelligent Mechatron Syst Res Unit Dept Mechatron Engn Kuala Lumpur Malaysia

This paper proposes a feature extraction method named as LP_QR, based on the decomposition of the LPC filter impulse response matrix of the signal of interest. This feature extraction method is inspired by LP_SVD and is tested in the context of motor imagery electroencephalogram. The extracted features are classified and benchmarked against extracted features of LP_SVD method. The two applied methods are also compared regarding the required execution time, which further highlights their respective merits and demerits. This paper closely examines the contribution of EEG channels of these two information extraction algorithms too. Consequently, a detailed analysis of the role of EEG channels concerning the nature of the extracted information is presented. This study is conducted on the BCI Ilia competition database of four motor imagery movements. The obtained results indicate that the proposed method is the better choice if simplicity is demanded. The investigation into the role of EEG channels reveals that level of contribution each channel can be quite dissimilar for different feature extraction algorithms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： EEG linear prediction coding QR decomposition Singular value decomposition Channel selection

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LOW-COMPLEXITY PARCOR COEFFICIENT QUANTIZER AND prediction ORDER ESTIMATOR FOR LOSSLESS SPEECH coding

LOW-COMPLEXITY PARCOR COEFFICIENT QUANTIZER AND PREDICTION O...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Yutaka Kamamoto Takehiro Moriya Noboru Harada NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation JAPAN

ISBN: (纸本)9781424442959

This paper describes two low-complexity tools used for the new ITU-T recommendation G.711.0, the lossless coding of G.711 (A-law/mu-law logarithmic PCM) speech data. One is an algorithm for quantizing the PARCOR/reflection coefficients and the other is an estimation method for the optimal prediction order. Both tools are based on a criterion that minimizes the entropy of the prediction residual signals and can be implemented in a fixed-point low-complexity algorithm. G.711.0 with the developed practical tools will be widely used everywhere because it can losslessly reduce the data rate of G.711, the prevailing speech-coding technology.

关键词： Speech coding Lossless compression Standardization linear predictive coding G.711 lossless compression speech coding quantizer linear prediction coding Standardization Speech Data transfer rates

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