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Digital Speech Processing— Lecture 17
Digital Speech Processing— Lecture 17 Speech Coding Methods Based on Speech Models 1 Waveform Coding versus Block Processing • Waveform coding – sample-by-sample matching of waveforms – coding quality measured using SNR • Source modeling (block processing) – block processing of signal => vector of outputs every block – overlapped blocks Block 1 Block 2 Block 3 2 Model-Based Speech Coding • we’ve carried waveform coding based on optimizing and maximizing SNR about as far as possible – achieved bit rate reductions on the order of 4:1 (i.e., from 128 Kbps PCM to 32 Kbps ADPCM) at the same time achieving toll quality SNR for telephone-bandwidth speech • to lower bit rate further without reducing speech quality, we need to exploit features of the speech production model, including: – source modeling – spectrum modeling – use of codebook methods for coding efficiency • we also need a new way of comparing performance of different waveform and model-based coding methods – an objective measure, like SNR, isn’t an appropriate measure for model- based coders since they operate on blocks of speech and don’t follow the waveform on a sample-by-sample basis – new subjective measures need to be used that measure user-perceived quality, intelligibility, and robustness to multiple factors 3 Topics Covered in this Lecture • Enhancements for ADPCM Coders – pitch prediction – noise shaping • Analysis-by-Synthesis Speech Coders – multipulse linear prediction coder (MPLPC) – code-excited linear prediction (CELP) • Open-Loop Speech Coders – two-state excitation -
Mpeg Vbr Slice Layer Model Using Linear Predictive Coding and Generalized Periodic Markov Chains
MPEG VBR SLICE LAYER MODEL USING LINEAR PREDICTIVE CODING AND GENERALIZED PERIODIC MARKOV CHAINS Michael R. Izquierdo* and Douglas S. Reeves** *Network Hardware Division IBM Corporation Research Triangle Park, NC 27709 [email protected] **Electrical and Computer Engineering North Carolina State University Raleigh, North Carolina 27695 [email protected] ABSTRACT The ATM Network has gained much attention as an effective means to transfer voice, video and data information We present an MPEG slice layer model for VBR over computer networks. ATM provides an excellent vehicle encoded video using Linear Predictive Coding (LPC) and for video transport since it provides low latency with mini- Generalized Periodic Markov Chains. Each slice position mal delay jitter when compared to traditional packet net- within an MPEG frame is modeled using an LPC autoregres- works [11]. As a consequence, there has been much research sive function. The selection of the particular LPC function is in the area of the transmission and multiplexing of com- governed by a Generalized Periodic Markov Chain; one pressed video data streams over ATM. chain is defined for each I, P, and B frame type. The model is Compressed video differs greatly from classical packet sufficiently modular in that sequences which exclude B data sources in that it is inherently quite bursty. This is due to frames can eliminate the corresponding Markov Chain. We both temporal and spatial content variations, bounded by a show that the model matches the pseudo-periodic autocorre- fixed picture display rate. Rate control techniques, such as lation function quite well. We present simulation results of CBR (Constant Bit Rate), were developed in order to reduce an Asynchronous Transfer Mode (ATM) video transmitter the burstiness of a video stream. -
Efficient Multi-Carrier Communication on the Digital Subscriber Loop
Efficient Multi-Carrier Communication on the Digital Subscriber Loop Donnacha Daly Department of Electrical and Electronic Engineering Faculty of Engineering and Architecture University College Dublin National University of Ireland A thesis presented to the National University of Ireland Faculty of Engineering and Architecture in fulfillment of the requirements for the degree of Doctor of Philosophy May 2003 Research Supervisors: Dr. C. Heneghan Prof. A. D. Fagan Head of Department: Prof. T. Brazil Abstract This thesis explores three distinct philosophies for improving the efficiency of multi-carrier com- munication on the digital subscriber loop. The first topic discussed is impulse response shortening for discrete multitone transceivers. The minimum mean-squared error impulse response shortener is reformulated to allow near-optimal rate performance. It is demonstrated that the best existing eigen-filter designed channel shortener is a particular case of the proposed reformulation. An adap- tive time-domain LMS algorithm is provided as an alternative to eigen-decomposition. The next part of the thesis examines bit- and power- loading algorithms for multitone systems. The problem of rate-optimal loading has already been solved. It is shown, however, that the rate-optimal solu- tion does not give best value for complexity, and that near optimal schemes can perform very well at a fraction of the computational cost. The final section of the thesis is a brief exposition of the use of wavelet packets to achieve multi-carrier communication. It is proposed that the non-uniform spectral decomposition afforded by wavelet packet modulation allows reduced inter-symbol inter- ference effects in a dispersive channel. -
16.1 Digital “Modes”
Contents 16.1 Digital “Modes” 16.5 Networking Modes 16.1.1 Symbols, Baud, Bits and Bandwidth 16.5.1 OSI Networking Model 16.1.2 Error Detection and Correction 16.5.2 Connected and Connectionless 16.1.3 Data Representations Protocols 16.1.4 Compression Techniques 16.5.3 The Terminal Node Controller (TNC) 16.1.5 Compression vs. Encryption 16.5.4 PACTOR-I 16.2 Unstructured Digital Modes 16.5.5 PACTOR-II 16.2.1 Radioteletype (RTTY) 16.5.6 PACTOR-III 16.2.2 PSK31 16.5.7 G-TOR 16.2.3 MFSK16 16.5.8 CLOVER-II 16.2.4 DominoEX 16.5.9 CLOVER-2000 16.2.5 THROB 16.5.10 WINMOR 16.2.6 MT63 16.5.11 Packet Radio 16.2.7 Olivia 16.5.12 APRS 16.3 Fuzzy Modes 16.5.13 Winlink 2000 16.3.1 Facsimile (fax) 16.5.14 D-STAR 16.3.2 Slow-Scan TV (SSTV) 16.5.15 P25 16.3.3 Hellschreiber, Feld-Hell or Hell 16.6 Digital Mode Table 16.4 Structured Digital Modes 16.7 Glossary 16.4.1 FSK441 16.8 References and Bibliography 16.4.2 JT6M 16.4.3 JT65 16.4.4 WSPR 16.4.5 HF Digital Voice 16.4.6 ALE Chapter 16 — CD-ROM Content Supplemental Files • Table of digital mode characteristics (section 16.6) • ASCII and ITA2 code tables • Varicode tables for PSK31, MFSK16 and DominoEX • Tips for using FreeDV HF digital voice software by Mel Whitten, KØPFX Chapter 16 Digital Modes There is a broad array of digital modes to service various needs with more coming. -
Speech Compression
information Review Speech Compression Jerry D. Gibson Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93118, USA; [email protected]; Tel.: +1-805-893-6187 Academic Editor: Khalid Sayood Received: 22 April 2016; Accepted: 30 May 2016; Published: 3 June 2016 Abstract: Speech compression is a key technology underlying digital cellular communications, VoIP, voicemail, and voice response systems. We trace the evolution of speech coding based on the linear prediction model, highlight the key milestones in speech coding, and outline the structures of the most important speech coding standards. Current challenges, future research directions, fundamental limits on performance, and the critical open problem of speech coding for emergency first responders are all discussed. Keywords: speech coding; voice coding; speech coding standards; speech coding performance; linear prediction of speech 1. Introduction Speech coding is a critical technology for digital cellular communications, voice over Internet protocol (VoIP), voice response applications, and videoconferencing systems. In this paper, we present an abridged history of speech compression, a development of the dominant speech compression techniques, and a discussion of selected speech coding standards and their performance. We also discuss the future evolution of speech compression and speech compression research. We specifically develop the connection between rate distortion theory and speech compression, including rate distortion bounds for speech codecs. We use the terms speech compression, speech coding, and voice coding interchangeably in this paper. The voice signal contains not only what is said but also the vocal and aural characteristics of the speaker. As a consequence, it is usually desired to reproduce the voice signal, since we are interested in not only knowing what was said, but also in being able to identify the speaker. -
Advanced Speech Compression VIA Voice Excited Linear Predictive Coding Using Discrete Cosine Transform (DCT)
International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2 Issue-3, February 2013 Advanced Speech Compression VIA Voice Excited Linear Predictive Coding using Discrete Cosine Transform (DCT) Nikhil Sharma, Niharika Mehta Abstract: One of the most powerful speech analysis techniques LPC makes coding at low bit rates possible. For LPC-10, the is the method of linear predictive analysis. This method has bit rate is about 2.4 kbps. Even though this method results in become the predominant technique for representing speech for an artificial sounding speech, it is intelligible. This method low bit rate transmission or storage. The importance of this has found extensive use in military applications, where a method lies both in its ability to provide extremely accurate high quality speech is not as important as a low bit rate to estimates of the speech parameters and in its relative speed of computation. The basic idea behind linear predictive analysis is allow for heavy encryptions of secret data. However, since a that the speech sample can be approximated as a linear high quality sounding speech is required in the commercial combination of past samples. The linear predictor model provides market, engineers are faced with using other techniques that a robust, reliable and accurate method for estimating parameters normally use higher bit rates and result in higher quality that characterize the linear, time varying system. In this project, output. In LPC-10 vocal tract is represented as a time- we implement a voice excited LPC vocoder for low bit rate speech varying filter and speech is windowed about every 30ms. -
(12) United States Patent (10) Patent No.: US 6,665,546 B2 Slaughter Et Al
USOO6665546B2 (12) United States Patent (10) Patent No.: US 6,665,546 B2 Slaughter et al. (45) Date of Patent: *Dec. 16, 2003 (54) HIGH SPEED, POINT-TO-POINT, (56) References Cited MILLIMETER WAVE DATED COMMUNICATION SYSTEM U.S. PATENT DOCUMENTS 5,701,591 A * 12/1997 Wong .......................... 455/63 (75) Inventors: Louis Slaughter, Weston, MA (US); 5,890.055 A * 3/1999 Chu et al...................... 455/23 Jon Hill, Socorro, NM (US); Thomas 5.936,578 A * 8/1999 Driessen et al. .............. 455/65 Lambert, Makawao, HI (US); Huan Nguyen, Annandale, VA (US); Randall (List continued on next page.) Olsen, Carlsbad, CA (US); John Lovberg, San Diego, CA (US); Primary Examiner William Trost Kenneth Y. Tang, Alpine, CA (US); ASSistant Examiner Rafael Perez-Gutierrez Vladimir Kolinko, San Diego, CA (74) Attorney, Agent, or Firm John R. Ross; John R. Ross, (US) III (73) Assignee: Trex Enterprises Corporation, San (57) ABSTRACT Diego, CA (US) A point-to-point, wireless, millimeter wave trunk line com (*) Notice: Subject to any disclaimer, the term of this munications link at high data rates in excess of 1 Gbps and patent is extended or adjusted under 35 at ranges of Several miles during normal weather conditions. U.S.C. 154(b) by 0 days. This link is combined with an Ethernet network to provide high Speed digital data communication among a large num This patent is Subject to a terminal dis ber of users. In a preferred embodiment a trunk line com claimer. munication link operates within the 92 to 95 GHZ portion of the millimeter spectrum. -
Lessons from Telco & Wireless Providers
LESSONS FROM TELCO & WIRELESS PROVIDERS: EXTENDING THE LIFE OF THE HFC PLANT WITH NEW TECHNOLOGIES Tom Cloonan, Ayham Al-Banna, Mike Emmendorfer, Zoran Maricevic, Frank O’Keeffe, John Ulm ARRIS Group, Inc. Abstract BACKGROUND This paper draws on lessons from the past (within the telecommunications space) to Introduction predict some of the new technologies that may be considered by Multiple System Operators Correctly predicting the future is a difficult, (MSOs) as they move forward into a service but critical task for any company. This is provider world of the future. It is a future that especially true when an industry is facing a will undoubtedly demand more and more time when transitions in technologies are bandwidth to be offered to subscribers over being considered- with the potential end-of- time. life for one technology approaching and prospects for the birth of a new technology After presenting some historical data on the looming in the foreseeable future. Are there evolution of telecommunications systems and ways to extend the life of the existing some new traffic engineering information on technology? Are those extensions beneficial bandwidth growth trends, the paper will or not? If a transition is to take place, when attempt to identify the life-span of the current should it take place? Should the transition be Hybrid Fiber/Coax (HFC) infrastructure done quickly or gradually? Which through which most Voice, Video, and Data technologies should be used during the services will be provided in the future. transition? Which of many available Potential techniques for extending that life- technologies should carry the load in the span will also be explored. -
Source Coding Basics and Speech Coding
Source Coding Basics and Speech Coding Yao Wang Polytechnic University, Brooklyn, NY11201 http://eeweb.poly.edu/~yao Outline • Why do we need to compress speech signals • Basic components in a source coding system • Variable length binary encoding: Huffman coding • Speech coding overview • Predictive coding: DPCM, ADPCM • Vocoder • Hybrid coding • Speech coding standards ©Yao Wang, 2006 EE3414: Speech Coding 2 Why do we need to compress? • Raw PCM speech (sampled at 8 kbps, represented with 8 bit/sample) has data rate of 64 kbps • Speech coding refers to a process that reduces the bit rate of a speech file • Speech coding enables a telephone company to carry more voice calls in a single fiber or cable • Speech coding is necessary for cellular phones, which has limited data rate for each user (<=16 kbps is desired!). • For the same mobile user, the lower the bit rate for a voice call, the more other services (data/image/video) can be accommodated. • Speech coding is also necessary for voice-over-IP, audio-visual teleconferencing, etc, to reduce the bandwidth consumption over the Internet ©Yao Wang, 2006 EE3414: Speech Coding 3 Basic Components in a Source Coding System Input Transformed Quantized Binary Samples parameters parameters bitstreams Transfor- Quanti- Binary mation zation Encoding Lossy Lossless Prediction Scalar Q Fixed length Transforms Vector Q Variable length Model fitting (Huffman, …... arithmetic, LZW) • Motivation for transformation --- To yield a more efficient representation of the original samples. ©Yao Wang, 2006 EE3414: -
Linear Predictive Coding Is All-Pole Resonance Modeling
Linear Predictive Coding is All-Pole Resonance Modeling Hyung-Suk Kim Center for Computer Research in Music and Acoustics, Stanford University 1 Why another article on LPC? Linear predictive coding (LPC) is a widely used technique in audio signal pro- cessing, especially in speech signal processing. It has found particular use in voice signal compression, allowing for very high compression rates. As widely adopted as it is, LPC is covered in many textbooks and is taught in most ad- vanced audio signal processing courses. So why another article on LPC? Despite covering LPC during my undergraduate coursework in electrical en- gineering, it wasn't until implementing LPC for my own research project that I understood what the goals of LPC were and what it was doing mathematically to meet those goals. A great part of the confusion stemmed from the some- what cryptic name, Linear-Predictive-Coding. \Linear" made sense, however \Predictive" and \Coding", sounded baffling, at least to the undergraduate me. What is it trying to predict? And coding? What does that mean? As we will find in the following sections, the name LPC does make sense. However, it is one catered to a specific use, speech signal transmission, possibly obscuring other applications, such as cross-synthesis. It takes a moment to understand the exclamation, \I used linear predictive coding to cross-synthesize my voice with a creaking ship!". Thus the purpose of this article is to explain LPC from a general perspective, one that makes sense to me and hopefully to others trying to grasp what LPC is. -
English Help File by Colin Bell, 2E0BPP. To
MixW Help Contents 25-Jul-2017 _________________________________________________________ *OVERVIEW OF MIXW 1. Welcome to MixW -- Information about the Program 2. Quick Start -- For experienced digital mode users 3. Registration -- How to become a registered user 4. Using the MixWHelp System -- Finding Information! *CONFIGURATION & SET UP 1. Configuration -- Software Settings 2. Basic Set Up -- PC/Tcvr Interface 3. PTT Circuit -- Hardware Connection 4. Configuring Macros -- Operating Efficiently *OPERATION 1. Starting Mixw - how to start Mixw 2. General Operation - for all modes 3. File Menu Items - short descriptions 4. Edit Menu Items - short descriptions 5. Options Menu Items - short descriptions 6. View Menu Items - short descriptions 7. Using the Status Bar - essential how-to 8. Logging and QSLing - essential how-to 9. Saving and Archiving - files changed for Mixw running *DIGITAL MODES CW FAX RTTY Amtor Packet Pactor PSK MFSK THROB FSK MT63 SSTV Hellschreiber Olivia Contestia RTTYM *APPENDICES 1. Cat Bar/Cat config and Bands.ini 2. Contest Operation 3. DX Cluster 4. FAQ's 5. File Descriptions 6. HF Digital Modes Band Plan 7. Keyboard Shortcuts 8. Macro Commands 9. MixW External Resources 10. MixW Installation 11. MixW Release History 12. QSLPRINT.EXE 13. Script Commands 14. The Eye of a Needle (TEOAN) 15. TNC Configuration and Operation 16. Using MixW Voice Keying 17. Using MixW with DXAtlas 18. Using MixW with other programs, DDE 19. Using the Spectrum Display 20. Using the Waterfall--Step by Step *Help Index *OVERVIEW OF MIXW _________________________________________________________ 1. Welcome to MixW -- Information about the Program 2. Quick Start -- For experienced digital mode users 3. Registration -- How to become a registered user 4. -
History of APRS 1992 APRSTM Was First Introduced by Bob Bruninga, WB4APR, in the Fall of 1992 at the ARRL Computer Networking Conference in Teaneck, New Jersey
CONFERENCE Puget Sound Amateur Radio TCP/IP Group Boeing Employees Amateur Radio Society (BEARS) ’ v - Conference Coordinators: / Steve Stroh, N8GNJ Keith Justice, KF7TP Steve Ford, WBSIMY Greg Jones, WD5IVD American Radio Relay League, Inc. 225 Main Street Newington, CT 06111-1494 USA tel: 860-594-0200 WWW: http:llwww.arrl.org/ Tucson Amateur Packet Radio 8987-309 E. Tanque Verde Rd #337 Tucson, Arizona 85749-9399 USA tel: 817-383-000 WWW: http://www.tapr.org Copyright 0 1996 by The American Radio Relay League, Inc. Copyright secured under the Pan-American Convention International Copyright secured This work is Publication Number 244 of the Radio Amateur’s Library, published by the League. All rights reserved. No part of this work may be reproduced in any form except by written permission of the publisher. All rights of translation reserved. Printed in USA Quedan reservados todos 10s derechos ISBN: O-87259-568-4 ARRL Order Number: 5684 First Edition On Amateur Digital Communications This is the first time I’ve participated in an ARRL Digital Communications Conference. I know that these conferences have served as a sounding board for technical ideas. Some have become standards and accepted by the mainstream. Amateur packet radio is certainly an example of amateurs contributing to the state-of-the-art. The League is now faced with increasing difficulty justifying our precious spectrum. It doesn’t at all reflect poorly on amateurs. The problem is that commercial services are seeking spectrum, on a shared basis if necessary, when they can’t get exclusive allocations. If you’d asked me earlier this year if extensive amateur use of a band would protect it against encroachment, I would have said “yes.” You will remember the saying, “Use it or lose it.” Well, we certainly can lose a band by not using it.