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

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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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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Gain-optimized spectral distortions for pronunciation training

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OPTIMIZATION LETTERS 2022年第7期16卷 2095-2113页

作者： Savchenko, Andrey V. Savchenko, Vladimir V. Savchenko, Lyudmila V. HSE Univ Lab Algorithms & Technol Network Anal Nizhnii Novgorod Russia Nizhny Novgorod State Linguist Univ Nizhnii Novgorod Russia HSE Univ Nizhnii Novgorod Russia

This paper considers an assessment and evaluation of speech sound pronunciation quality in computer-aided language learning systems. We examine the gain optimization of spectral distortion measures between the speech signals of a native speaker and a learner. During training, a learner has to achieve stable pronunciation of all sounds. This is measured by computing the distances between the sounds produced by the learner and the model speaker. In order to improve pronunciation, it is proposed to adapt the linear prediction coding coefficients of reference sounds by using the gradient descent optimization of the gain-optimized dissimilarity. As a result, we demonstrate the possibility of synthesizing sounds that will be either close to the model pronunciation or achievable by a learner. An experimental study shows that the proposed procedure leads to high efficiency for pronunciation training even in the presence of noise in the observed utterance.

关键词： Computer-aided language learning Computer-assisted pronunciation tool Speech quality assessment Itakura-Saito divergence Gain optimization linear prediction coding Autoregressive model

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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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Compressing animated meshes with fine details using local spectral analysis and deformation transfer

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VISUAL COMPUTER 2020年第5期36卷 1029-1042页

作者： Chen, Chengju Xia, Qing Li, Shuai Qin, Hong Hao, Aimin Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China SUNY Stony Brook Dept Comp Sci New York NY USA

Geometry-centric shape animation, usually represented as dynamic meshes with fixed connectivity and time-deforming geometry, is becoming ubiquitous in digital entertainment and other relevant graphics applications. However, digital animation with fine details, which requires more diversity of texture on meshed geometry, always consumes a significant amount of storage space, and compactly storing and efficiently transmitting these meshes still remain technically challenging. In this paper, we propose a novel key-frame-based dynamic meshes compression method, wherein we decompose the meshes into the low-frequency and high-frequency parts by applying piece-wise manifold harmonic bases to reduce spatial-temporal redundancy of primary poses and by using deformation transfer to recover high-frequency details. First of all, we partition the animated meshes into several clusters with similar poses, and the primary poses of meshes in each cluster can be characterized as a linear combination of manifold harmonic bases derived from the key-frame of that cluster. Second, we recover the geometric details on each primary pose using the deformation transfer technique which reconstructs the details from the key-frames. Thus, we only need to store a very small number of key-frames and a few harmonic coefficients for compressing time-varying meshes, which would reduce a significant amount of storage in contrast with traditional methods where bases were stored explicitly. Finally, we employ the state-of-the-art static mesh compression method to store the key-frames and apply a second-order linear prediction coding to the harmonics coefficients to further reduce the spatial-temporal redundancy. Our comprehensive experiments and thorough evaluations on various datasets have manifested that, our novel method could obtain a high compression ratio while preserving high-fidelity geometry details and guaranteeing limited human perceived distortion rate simultaneously, as quantitatively chara

关键词： Animated mesh compression Manifold harmonic basis Deformation transfer linear prediction coding

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Improved Silence-Unvoiced-Voiced (SUV) Segmentation for Dysarthric Speech Signals using linear prediction Error Variance 5

Improved Silence-Unvoiced-Voiced (SUV) Segmentation for Dysa...

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IEEE 5th International Conference on Computer and Communication Systems (ICCCS)

作者： Ijitona, Tolulope Yue, Hong Soraghan, John Lowit, Anja Univ Strathclyde Dept Elect & Elect Engn Glasgow Lanark Scotland Univ Strathclyde Sch Psychol Sci & Hlth Glasgow Lanark Scotland

ISBN: (纸本)9781728161365

A novel algorithm for the segmentation of dysarthric speech into silence, unvoiced and voiced (SUV) segments is presented. The proposed algorithm is based on the combination of short-time energy (STE), zero-crossing rate (ZCR) and linear prediction error variance (LPEV) or the segmentation problem. Extending the previous work in this field, the proposed method will address the difficulties in distinguishing between voiced and unvoiced segments in dysarthric speech. More precisely, the error variance of the linear prediction coefficients will be used to design a three-fold decision matrix that can accommodate the high variability in loudness experienced in dysarthric speech. In addition, a moving average threshold approach will be proposed in order to provide an "as-fit" segmentation technique that is fully automated and that will be able to handle highly severe dysarthric speech with varying loudness and ZCRs. The ability of the proposed fully-automated algorithm will be validated using real speech samples from healthy speakers, and speakers with ataxic dysarthria. The results of the proposed approach are compared with known methods using STE and ZCR. It is observed that the proposed classification method does not only show an improvement in segmentation performance but also provides consistent results in low signal energy situations.

关键词： Dysarthria Speech Disorder linear prediction Error Variance Zero Crossing Rate SUV Voiced-Unvoiced linear prediction coding

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Automatic Speech Recognition bay System MFCC-LPC-SVM for energy optimization in intelligent building

Automatic Speech Recognition bay System MFCC-LPC-SVM for ene...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Henni Sid Ahmed Caelen Jean dept. Geni Electrica faculty science and technology University Abdelhamid Ibn Badis–Mostaganemm Route nationale Mostaganemmm Algeria LSI Carnot Institute (Software an Intelligent Systems Laboratory (LIG)CTL – Center for Software Technologies University Joseph Fourier – MultiCom Gières Grenoble France

ISBN: (纸本)9781728131573

the problem of smart buildings is their overconsumption of energy; in this article we propose a solution to this problem. This solution is to give optimum comfort to the people who occupy the smart buildings, but sometimes these people are not satisfied with this comfort, so we realized a system of automatic speech recognition, to give the possibility for these people to change the parameters of comfort, and to obtain the desired comfort. This solution allows to optimize the smart building energy, for example if a room is empty, then the person who has been in this room must say off when leaving this room, in this case the automatic recognition system of speech, instructs the system that managed the energy to turn off the lamps and lower the heater or air conditioner. This automatic speech recognition system using the MFCC method (Mel frequency cepstral coefficient) and a LPC method (linear prediction coding) for the representation of the speech signal, and SVM (support vector machine), for speech recognition. The recognition will be of global type, the speech signal will be parameterized using the MFCC coefficients and LPC coefficients to form the input vector of the recognition system, the SVM will be used for the learning phase and recognition phase. An experiment will be implemented using an input corpus, the vocabulary used in this corpus consists of 10 words in English language. The results obtained will be analyzed and compared with other similar works that use other techniques of automatic speech recognition.

关键词： MFCC LPC Speech processing SVM Classification Energy Intelligent building intelligent buildings Speech recognition Mel frequency cepstral coefficient linear prediction coding PCSK7 gene Support Vector Network Speech processing Energy sources Voice Signal COMFORT Taxonomy recognition system Persons

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