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33rd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： De Meuleneire, M. Taddei, H. Pastor, D. Nokia Siemens Networks COO RTP PT HWCT Comp Techn SDE Otto Hahn Ring 6 D-81739 Munich Germany ENST Bretagne Technop Brest Irois Dept SC F-29238 Brest 3 France

ISBN: (纸本)9781424414833

This paper proposes a new structure for a scalable codec. Our proposed codec works with 10 ms input frame for wideband speech and audio signals at bit rates ranging from 8 to 32 kbit/s. The core layer is the ITU-T G.729 at 8 kbit/s producing a narrowband output. The first enhancement layer is a band-witdh extension providing a wideband output with 2 kbit/s. The second enhancement layer is based on algebraic quantization of wavelet packet coefficients and improves gradually the synthesized signal as the bitrate increases. For speech signals, at bitrates of 24 and 32 kbit/s, the codec is shown to be equivalent to the ITU-T G.722 codec at 56 and 64 kbit/s, respectively. Moreover, the codec at 32 kbit/s is assessed to be equivalent to the recently standardized embedded codec ITU-T G.729.1 at the same bitrate with a lower algorithmic delay.

关键词： embedded coding linear predictive coding bandwidth extension wavelets algebraic quantization

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Ultrasound- and MRI-based Speech Synthesis Applying Neural Networks 25

Ultrasound- and MRI-based Speech Synthesis Applying Neural N...

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25th International Carpathian Control Conference (ICCC)

作者： Trencsenyi, Reka Czap, Laszlo Univ Debrecen Dept Elect & Elect Engn Debrecen Hungary Univ Miskolc Inst Automat & Infocommun Miskolc Hungary

ISBN: (纸本)9798350350708;9798350350715

Starting from 2D dynamic ultrasound and MRI sources recording the movement of the vocal organs and the speech signal of the speaker in a simultaneous and synchronised manner, we produce machine speech by means of artificial intelligence. As visual objects, we use tongue and palate contours fitted automatically to the anatomic boundaries of the ultrasound and MRI images, and for training, we extract geometric information from these contours, as the change of their shape fundamentally describes the movement of the vocal organs during articulation. The geometric data consist of radial distances between the tongue and palate contours and coefficients of the discrete cosine transform of the curves, respectively. Relying on this dataset, parameters connected to the acoustic content of the speech signal are trained by the network. These parameters can be interpreted in the framework of the acoustic tube model of the vocal tract, and according to this, reflection coefficients and areas of the articulation channel are to be trained. In this study, vowels, words, and sentences are synthesised using linear predictive coding and the acoustic tube model.

关键词： speech synthesis acoustic tube model linear predictive coding neural networks anatomic contour tracking ultrasound and MRI records

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On Reflection Coefficients and the Cholesky Decomposition

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IEEE Transactions on Acoustics, Speech, and Signal Processing 1977年第1期25卷 93-96页

作者： Gibson, Jerry D. Department of Electrical Engineering University of Nebraska Lincoln NE 68588 United States

The autocorrelation and covariance methods are the two most popular analysis techniques for linear predictive coding of speech. Recent work on the covariance method has utilized the Cholesky decomposition for solving the normal equations. A set of intermediate parameters in the Cholesky approach closely resembles the reflection coefficients in the autocorrelation method, and some workers have treated them as such since several computational and operational advantages thus accrue. The Cholesky decomposition is examined in this paper, and it is shown that when the covariance matrix is not Toeplitz, the intermediate parameters are not the reflection coefficients and do not behave like the reflection coefficients. It is also shown that when the covariance matrix is Toeplitz and the vector of dependent variables consists of autocorrelation terms, the intermediate parameters can be interpreted as reflection coefficients. Copyright © 1977 by The Institute of Electrical and Electronics Engineers, Inc.

关键词： Reflection Autocorrelation Speech analysis Covariance matrix Filters Stability linear predictive coding Speech coding Equations Signal processing algorithms

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Web Based Voice Assistant for Railways Using Deep Learning Approach 2nd

Web Based Voice Assistant for Railways Using Deep Learning A...

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2nd International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)

作者： Vadamodula, Prasad Cristin, R. Daniya, T. GMRIT Dept Comp Sci & Engn Rajam India GMRIT Dept Informat Technol Rajam India

ISBN: (纸本)9789811925412;9789811925405

Speech recognition is the ability of machine or program to respond to spoken commands. It works using algorithms through acoustic and language modelling. It is a technique which enables hands-free control of various devices to interact with machines. This project deals with developing a speaking machine using the deep learning algorithm named linear predictive coding (LPC) or linear predictive Speech Synthesis (LPSS) which can be used by customers or passengers in a railway station for an enquiry. This helps the people to enquire the details of trains for the limited questionnaire, identified by the name of the train with reference station location and its availability of trains at the real-time for required destinations. This system gives the information about the platform number for all the train arrivals with the names of the trains and the details of the ticket fare for different destinations. These can be done through giving the voice input to the developed system.

关键词： Communication Deep learning Interaction and speech recognition linear predictive coding linear predictive speech synthesis

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An ERP study of face expression

An ERP study of face expression

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2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, NFSI and ICFBI 2007

作者： Yufang, Wei Guizhi, Xu Yang, Song Lei, Wang Shuo, Yang Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology Tianjin 300130 China

ISBN: (纸本)1424409489

This study combined behavioral performance, eventrelated potential (ERP) and explored the differences between positive expression and negative expression in face domain. A "study-test" pattern was adopted. At study phase, subjects were required to memorize stimuli. At recognition phase, task was old/new discrimination. The results indicated that, special components of face which included N170, VPP, and the later P200, N270, LPC were found in the ERP waves of face expression. In 450-850ms, there were differences between the late components caused by happy expression and angry expression. Hemisphere difference to the ERP wave of negative expression was found. © 2007 IEEE.

关键词： Enterprise resource planning Electrodes Electroencephalography Pressing Electromagnetic fields linear predictive coding Electrophysiology Scalp Humans Face recognition

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A new Gabor filter based kernel for texture classification with SVM

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International Conference on Image Analysis and Recognition

作者： Sabri, M Fieguth, P Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)3540232400

The performance of Support Vector Machines (SVMs) is highly dependent on the choice of a kernel function suited to the problem at hand. In particular, the kernel implicitly performs a feature selection which is the most important stage in any texture classification algorithm. In this work a new Gabor filter based kernel for texture classification with SVMs is proposed. The proposed kernel function is based on a Gabor filter decomposition and exploiting linear predictive coding (LPC) in each subband, and exploiting a filter selection method to choose the best filters. The proposed texture classification method is evaluated using several texture samples, and compared with recently published methods. The comprehensive evaluation of the proposed method shows significant improvement in classification error rate.

关键词： texture classification Support Vector Machine linear predictive coding Gabor filters segmentation

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Research on ASIC for multi-speaker isolated word recognition

Research on ASIC for multi-speaker isolated word recognition

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2nd International Conference on ASIC

作者： Xiong, B Sun, YH Institute of Microelectronics Tsinghua University Beijing China

ISBN: (纸本)7543909405

The ASIC for multi-speaker speech recognition is design in this paper. The LPC-derived cepstral coefficients are chosen as speech features. Templates are trained by K-means clustering algorithm. Two stage recognition system can not only improve recognition accuracy, but also reduce the delay. The first stage of recognition system uses speech spectrum difference(SSD) algorithm. The second stage uses DTW. The whole recognition system is design into ASIC on high level with VHDL and simulated in Powerview.

关键词： speech recognition linear predictive coding application specific integrated circuits cepstral analysis ASIC multi-speaker isolated word recognition LPC cepstral coefficients K-means clustering algorithm template training speech spectrum difference algorithm DTW high level design VHDL Powerview simulation

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A 2.4kbps MBE-LPC speech codec algorithm suitable for VLSI implementation

A 2.4kbps MBE-LPC speech codec algorithm suitable for VLSI i...

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2nd International Conference on ASIC

作者： Yang, HM Chen, HY Sun, YH The Institute of Microelectronics Tsinghua University Beijing China

ISBN: (纸本)7543909405

A speech code/decode algorithm which combines MBE and LPC speech model is proposed. In this model, the spectral envelope is represented using linear Prediction Coefficients, which are coded using Line Spectrum Frequencies (LSFs). It can operate at 2.4 kbps with much higher quality of synthesis speech than LPC-10e and less computation complexity than CELP, VSELP and so on. Therefore it is particularly attractive for VLSI implementation.

关键词： speech codecs VLSI linear predictive coding computational complexity speech codec algorithm VLSI implementation MBE LPC spectral envelope linear prediction coefficients line spectrum frequencies computation complexity multiband excitation 2.4 kbit/s

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Dialects, Sex-specificity, and Individual Recognition in the Vocal Repertoire of the Puerto Rican Parrot (Amazona vittata)

Dialects, Sex-specificity, and Individual Recognition in the...

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作者： Roberts, Briony Z. Jr. Virginia Tech | University

The following study is part of a larger study examining techniques that might be of use in the release program of the Puerto Rican Parrot (Amazona vittata), including marking, capturing, and radio-tracking. The portion of the study reported here documents the vocal behavior of A. vittata during the reproductive season and examines the possibility of using vocalizations to identify individuals, determine the sex of individuals and determine the location of an individual’s breeding territory. Objectives of this study included: 1) cataloguing and categorizing the vocal repertoire of A. vittata, 2) determining whether the vocal repertoire was sex-specific and region-specific and 3) determining if an individual’s vocal repertoire could be used to identify it. The vocal repertoire was characterized using a hierarchical method and 147 calls were described. The repertoire was found contain a high percentage (76 %) of graded calls. Evolutionary strategies that may explain the complexity of such a repertoire are discussed. The vocal repertoire was found to be both sex- and region-specific. Characteristics analyzed included time and frequency parameters of sonagrams. Three methods were used to determine the feasibility of vocal recognition of individuals. These methods included: bird-call pairing, sonagraphic analysis, and linear predictive coding. Sonagraphic analyses in combination with linear predictive coding techniques show the most promise as tools in voice recognition of the parrot, however, further research will be necessary to determine how reliable voice recognition may be as a method for identifying individuals in the field.

关键词： voice recognition dialects graded calls sonagrams vocal repertoire Amazona vittata Puerto Rican Parrot linear predictive coding Thesis

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Métodos de extração de atributos para detecção de crises epiléticas: uma abordagem comparativa

Métodos de extração de atributos para detecção de crise...

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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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