COMPARATIVE REVIEW BETWEEN CELP AND ACELP ENCODER FOR CDMA TECHNOLOGY

Similar documents
Overview of Code Excited Linear Predictive Coder

Simulation of Conjugate Structure Algebraic Code Excited Linear Prediction Speech Coder

EE482: Digital Signal Processing Applications

Speech Coding Technique And Analysis Of Speech Codec Using CS-ACELP

techniques are means of reducing the bandwidth needed to represent the human voice. In mobile

Chapter IV THEORY OF CELP CODING

Implementation of attractive Speech Quality for Mixed Excited Linear Prediction

Analysis/synthesis coding

Voice Excited Lpc for Speech Compression by V/Uv Classification

Comparison of CELP speech coder with a wavelet method

The Channel Vocoder (analyzer):

The Optimization of G.729 Speech codec and Implementation on the TMS320VC5402

ON-LINE LABORATORIES FOR SPEECH AND IMAGE PROCESSING AND FOR COMMUNICATION SYSTEMS USING J-DSP

Digital Speech Processing and Coding

Cellular systems & GSM Wireless Systems, a.a. 2014/2015

An objective method for evaluating data hiding in pitch gain and pitch delay parameters of the AMR codec

Vocoder (LPC) Analysis by Variation of Input Parameters and Signals

Speech Compression Using Voice Excited Linear Predictive Coding

LOSS CONCEALMENTS FOR LOW-BIT-RATE PACKET VOICE IN VOIP. Outline

Transcoding of Narrowband to Wideband Speech

DEPARTMENT OF DEFENSE TELECOMMUNICATIONS SYSTEMS STANDARD

Information. LSP (Line Spectrum Pair): Essential Technology for High-compression Speech Coding. Takehiro Moriya. Abstract

EE 225D LECTURE ON MEDIUM AND HIGH RATE CODING. University of California Berkeley

Enhanced Waveform Interpolative Coding at 4 kbps

International Journal of Advanced Engineering Technology E-ISSN

Flexible and Scalable Transform-Domain Codebook for High Bit Rate CELP Coders

Wideband Speech Coding & Its Application

Proceedings of Meetings on Acoustics

APPLICATIONS OF DSP OBJECTIVES

Low Bit Rate Speech Coding

6/29 Vol.7, No.2, February 2012

Lesson 8 Speech coding

Communications Theory and Engineering

Waveform Coding Algorithms: An Overview

EC 6501 DIGITAL COMMUNICATION UNIT - II PART A

Audio Signal Compression using DCT and LPC Techniques

MASTER'S THESIS. Speech Compression and Tone Detection in a Real-Time System. Kristina Berglund. MSc Programmes in Engineering

Wireless Communications

speech signal S(n). This involves a transformation of S(n) into another signal or a set of signals

DEPARTMENT OF INFORMATION TECHNOLOGY QUESTION BANK. Subject Name: Information Coding Techniques UNIT I INFORMATION ENTROPY FUNDAMENTALS

E : Lecture 8 Source-Filter Processing. E : Lecture 8 Source-Filter Processing / 21

Page 0 of 23. MELP Vocoder

EUROPEAN pr ETS TELECOMMUNICATION March 1996 STANDARD

Final draft ETSI EN V1.3.0 ( )

Mel Spectrum Analysis of Speech Recognition using Single Microphone

Enhanced Variable Rate Codec, Speech Service Options 3, 68, 70, and 73 for Wideband Spread Spectrum Digital Systems

3GPP TS V8.0.0 ( )

IMPLEMENTATION OF G.726 ITU-T VOCODER ON A SINGLE CHIP USING VHDL

Open Access Improved Frame Error Concealment Algorithm Based on Transform- Domain Mobile Audio Codec

REAL-TIME IMPLEMENTATION OF A VARIABLE RATE CELP SPEECH CODEC

University of Washington Department of Electrical Engineering Computer Speech Processing EE516 Winter 2005

Speech Synthesis; Pitch Detection and Vocoders

EC 2301 Digital communication Question bank

ENEE408G Multimedia Signal Processing

Comparison of Low-Rate Speech Transcoders in Electronic Warfare Situations: Ambe-3000 to G.711, G.726, CVSD

SILK Speech Codec. TDP 10/11 Xavier Anguera I Ciro Gracia

IMPROVED SPEECH QUALITY FOR VMR - WB SPEECH CODING USING EFFICIENT NOISE ESTIMATION ALGORITHM

Tree Encoding in the ITU-T G Speech Coder

Realization and Performance Evaluation of New Hybrid Speech Compression Technique

Adaptive Forward-Backward Quantizer for Low Bit Rate. High Quality Speech Coding. University of Missouri-Columbia. Columbia, MO 65211

Waveform Encoding - PCM. BY: Dr.AHMED ALKHAYYAT. Chapter Two

Components for Signal Compression

Mobile Communications TCS 455

Final draft ETSI EN V1.2.0 ( )

UNIVERSITY OF SURREY LIBRARY

Linear Predictive Coding *

ARIB STD-T V Audio codec processing functions; Extended Adaptive Multi-Rate - Wideband (AMR-WB+) codec; Transcoding functions

An Approach to Very Low Bit Rate Speech Coding

A Closed-loop Multimode Variable Bit Rate Characteristic Waveform Interpolation Coder

Optimization of Speech Recognition using LPC Technic

Golomb-Rice Coding Optimized via LPC for Frequency Domain Audio Coder

Universal Vocoder Using Variable Data Rate Vocoding

3GPP TS V5.0.0 ( )

sensors ISSN

SIMULATION VOICE RECOGNITION SYSTEM FOR CONTROLING ROBOTIC APPLICATIONS

Dilpreet Singh 1, Parminder Singh 2 1 M.Tech. Student, 2 Associate Professor

Telecommunication Electronics

Robust Linear Prediction Analysis for Low Bit-Rate Speech Coding

Techniques for low-rate scalable compression of speech signals

Voice and Audio Compression for Wireless Communications

Microcomputer Systems 1. Introduction to DSP S

Analog and Telecommunication Electronics

Improved signal analysis and time-synchronous reconstruction in waveform interpolation coding

Speech Enhancement using Wiener filtering

MODULATION AND MULTIPLE ACCESS TECHNIQUES

CHAPTER 3 Syllabus (2006 scheme syllabus) Differential pulse code modulation DPCM transmitter

General outline of HF digital radiotelephone systems

COMPRESSIVE SAMPLING OF SPEECH SIGNALS. Mona Hussein Ramadan. BS, Sebha University, Submitted to the Graduate Faculty of

Adaptive time scale modification of speech for graceful degrading voice quality in congested networks

Datenkommunikation SS L03 - TDM Techniques. Time Division Multiplexing (synchronous, statistical) Digital Voice Transmission, PDH, SDH

SNR Scalability, Multiple Descriptions, and Perceptual Distortion Measures

Apex Group of Institution Indri, Karnal, Haryana, India

Scalable Speech Coding for IP Networks

Introduction to Digital Communications System

EE482: Digital Signal Processing Applications

Basics of Error Correcting Codes

Speech synthesizer. W. Tidelund S. Andersson R. Andersson. March 11, 2015

DESIGN AND IMPLEMENTATION OF CELP SPEECH PROCESSING SYSTEM USING TMS320C30

Ninad Bhatt Yogeshwar Kosta

Impact of the GSM AMR Speech Codec on Formant Information Important to Forensic Speaker Identification

Transcription:

COMPARATIVE REVIEW BETWEEN CELP AND ACELP ENCODER FOR CDMA TECHNOLOGY V.C.TOGADIYA 1, N.N.SHAH 2, R.N.RATHOD 3 Assistant Professor, Dept. of ECE, R.K.College of Engg & Tech, Rajkot, Gujarat, India 1 Assistant Professor, Dept. of ECE, R.K.College of Engg & Tech, Rajkot, Gujarat, India 2 Assistant Professor, Dept. of ECE, L.E.College of Engg & Tech, Morbi, Gujarat, India 3 Abstract: This review paper presents for analysis between code excited linear prediction and algebraic code excited linear prediction speech coding techniques which is useful in wireless communication for compression of speech signal to improve the data rate. from this paper we understood CELP encoder, decoder & ACELP encoder, and decoder & differentiate both the coding technology. How it used in WCDMA wireless communication system, the major factor in speech coding is bit rate which is reduced in ACELP coder up to 4.6kbps.Compare to CELP coder. In many applications for transmission of voice signal it conversion is needed for that speech coder is used. Keywords: CELP (code excited linear prediction) and ACELP (algebraic code excited linear prediction) I. INTRODUCTION This paper presents comparative review between code excited linear prediction and algebraic code excited linear prediction speech coding techniques [1]. The growth of wireless technology user required high data rates and increases to transmission speed of data; we have to reduces the bit rate of data for that it is necessary to compress to speech. Different voice coding techniques are used to compress the speech signal in which code excited linear prediction techniques is used in WCDMA technology for compression of speech.in mobile technology bandwidth and conversion quality of speech signal is most argued parameter in any telephony system which generates digital representation of speech. The CELP coding operated under 8kbps and it goal is transmit the minimum amount of speech signal in codeword with minimum error is produced to synthesis the speech signal[2].the major application of compression of speech in mobile communication at side of encoder to transmit the speech signal in low bit rate. It allows longer message into speech code, and it also allows to user share the same bandwidth.[3]. Codebook excited linear prediction (CELP) was introduced by B.S. Anal and M.A. Schroeder at the 1984[4].To increases the speed of data transmission and data rates one more coding technology introduced in wireless communication which is algebraic code excited linear prediction techniques. In ACELP the speech signal transmit with minimum 4kbps speed so we have to reduces the bit rate more & more compare to other coding technology, the MATLAB tool is used to design ACELP algorithm, The tool is user-friendly and graphical user interface (GUI) that allows the student to study and verify through graphics the various aspects of the algorithm such as: the LP analysis, the open-loop pitch search, the adaptive codebook search (pitch search), the fixed codebook search, and the bit allocation patterns. We choose MATLAB as the implementation platform because it allows the user to easily understand the complex parts of the algorithm whose function is not a Major [5]. By the end of introduction section include the paper organization. This paper is organized as follow: Section I gives the introduction comparative review between code excited linear prediction and algebraic code excited linear prediction speech coding techniques. Section II is helpful to Characteristics of speech. Section III explains comparison of CELP and ACELP section V concludes the paper and followed by the references. Copyright to IJAREEIE www.ijareeie.com 603

II. Characteristics of speech: Speech energy vs. frequency Fig-[1] Voiced signal Voiced signal shown in fig[2], it shows the relatiionship between amplitude of speech and varying frequncy,and same fig[2] shows the relationship between amplitude of speech and varying time Fig[2] Unvoiced signal Unvoiced signal shown in fig[3], it shows the relatiionship between amplitude of speech and varying frequncy,and same fig[3] shows the relationship between amplitude of speech and varying time. Copyright to IJAREEIE www.ijareeie.com 604

Fig-[3] III. COMPARISON BETWEEN CELP AND ACELP CODER This coder is optimized by using a code book (look up table) to find the best match for the Signal. This method reduces the processing complexity and the required data transmission rate. In Fig-[4] shows the block diagram of CELP encoder in which shows the stochastic codebook and adaptive codebook means that generate array of bit patterns its output multiply into multiplier, In which linear predictive coder analyser and 10 th order linear predictive coder synthesizer which analyses and synthesis the speech signals. LPC analysis (order 10 th ) is used to subtracting the vocal tract component from speech signal. The pitch search analyzes the error speech signal. It is perceptually weighted by weighting filter and then compared to all the sequences in the pitch codebook. Low Delay CELP (LD-CELP) and Algebraic CELP (ACELP) are generally used in internet voice calls and cell phones. The CELP coder does not directly need an analysis stage [6]. Fig-[4], Block diagram of CELP encoder [7] Copyright to IJAREEIE www.ijareeie.com 605

Fig-[5], Block diagram of CELP encoder [7] In Fig shows the block diagram of CELP decoder, in which read out the stohastic indices and compute the speech codeword,pitch prediction filter weight the error of speech and reconstruct the speech. ACELP ENCODER Fig-[6], Block diagram of ACELP encoder [8] It has Short algorithmic delay, and also having Low bandwidth toll-quality coder, it has Provision for concealment of detected frame erasures. It reduce the channel error [9].The name Algebraic CELP implies the structure of the codebook used to select the excitation codebook vector [10]. The speech signal is analyzed for speech frames of 10ms corresponding to 80 samples at a sampling rate of 8000 samples per second. The five important stages associated with the encoding principle of CS-ACELP include: the pre-processing stage, the LP analysis stage, the open-loop pitch search, the closed-loop pitch search, and the algebraic codebook search.[11]. Copyright to IJAREEIE www.ijareeie.com 606

Fig-[6] LP ANALYSIS The LP analysis shown in fig, the speech signal S(n) applied to LP analysis block which consist of window and autocorrelation block, it is operated on 120 sample from the past speech frames and 80 sample from present speech frames and 40 sample from the feature frame. LP coefficient derived from autocorrelation coefficient from window speech by using the Levin ion Durbin algorithm. the vector quantizer used to produce the LSP coefficient which is reduced the weighted mean square error. Finally output of LP analysis is reflection coefficient, K and LSP indices L0,L1,L2,L3 [12]. Fig-[7] OPEN LOOP AND CLOSED LOOP PITCH ANALYSIS From Levinson-Durbin algorithm the reflection coefficients used to compute the adaptive Weight factors. Where, S (n) is the pre-processed speech, γ1 and γ2 are the adaptive weights, and ai,i = 1, 2,..., 10 are the unquantized LP Coefficients. the auto correlation of weighted speech signal sw(n) is produced and it passes from the open loop pitch delay stage[12]. Fig-[8] ACELP DECODER Copyright to IJAREEIE www.ijareeie.com 607

The ACELP decoder shown in fig, from the received bit stream parameter indices are extracted.these parameters are LSP coefficient. the LSP coefficient converted into LP coefficient for each subframe.the output of fixed codebook vector and adaptive codebook vector are adding with fixed gain.then speech signal is reconstructed by LP filtering and then it passed from post filtering stage [13]. IV. CONCLUSION From comparative review of CELP and ACELP coder analyses that thing it reduces the bit rate of compressed speech signal for transmission and reception and it also provide good quality of voice output. From this paper also get the knowledge about how to improve the coder efficiency and reduce the bit error. REFERENCES [1]. Venkatraman Atti and Andreas Spanias, Department of Electrical Engineering, MIDL TRCArizona State University, Tempe, AZ, 85287-7206 [2]. Shannon Wichman Department of Electrical Engineering The University of Texas at Dallas [3]. A. R. Sahab*1, M. Khoshroo*Islamic Azad University Lahijan Branchr [4].THOMAS E. TREMAIN, JOSEPH P. CAMPBELL, JR., VANOY C. WELCHU.S. Department of Defense, R5Fort Meade, Maryland, U.S. A. 20755-6000 [5]. Venkatraman Atti and Andreas Spanias Department of Electrical Engineering, MIDL TRCArizona State University, Tempe, AZ, 85287-7206, U.S.A [7].Joseph p. cambell,jr,vanoy C,welch,and thomas E.Tremain, An Expandaable Error protected 4800bps CELP coder (U.S. Federal standard 4800 bps Voice coder),u.s.dod. [6]. INRS- Te'lPcommunications3 Place du Commerce Ile des Soeurs, Que. CANADA H3E 1H6 [7].Joseph p. cambell,jr,vanoy C,welch,and thomas E.Tremain, An Expandaable Error protected 4800bps CELP coder (U.S. Federal standard 4800 bps Voice coder),u.s.dod. [8]. ITU-T G.729/G.729ACS-ACELP 8kbps Speech Coder by:lior Shadhan [9].ITU-T G.729/G.729A CS-ACELP 8kbps Speech Coder by: Lior Shadhan [10]. Venkatraman Atti and Andreas Spanias Department of Electrical Engineering, MIDL TRC [11]. Venkatraman Atti and Andreas Spanias Department of Electrical Engineering, MIDL TRC [12].Venkatraman Atti and Andreas Spanias Department of Electrical Engineering, MIDL TRC Arizona State University,Tempe, AZ, 85287-7206, U.S.A [13]. Thai Speech Coding Based On Conjugate-Structure Algebraic Code Excited Linear Prediction Algorithm Suphattharachai Chomphan Department of Electrical Engineering, Faculty of Engineering at Si Racha, Kasetsart University, 199 M.6, Tungsukhla, Si Racha, Chonburi, 20230, Thailand. BIOGRAPHY VIJAY.C.TOGADIYA: Received his B.E degree in Electronics and communication Department of V.V.P engineering college Rajkot, Gujarat, India in 2007. Presently he is pursuing for his M.TECH degree in Electronics and communication in R. K university, Rajkot, Gujarat, India. NISHIT N SHAH: Received his M.E degree in Electronics and communication Department of C.U.SHAH engineering college Wad van, Gujarat, India in 2010. R.N.RATHOD: Received his M.E degree in Electronics and communication Department of L.D engineering college Ahmadabad, Gujarat, India in 2008. Copyright to IJAREEIE www.ijareeie.com 608