Principles of Speech Coding

It is becoming increasingly apparent that all forms of communication—including voice—will be transmitted through packet-switched networks based on the Internet Protocol (IP). Therefore, the design of modern devices that rely on speech interfaces, such as cell phones and PDAs, requires a complete and up-to-date understanding of the basics of speech coding. Outlines key signal processing algorithms used to mitigate impairments to speech quality in VoIP networks Offering a detailed yet easily accessible introduction to the field, Principles of Speech Coding provides an in-depth examination of the underlying signal processing techniques used in speech coding. The authors present coding standards from various organizations, including the International Telecommunication Union (ITU). With a focus on applications such as Voice-over-IP telephony, this comprehensive text covers recent research findings on topics including: A general introduction to speech processing Digital signal processing concepts Sampling theory and related topics Principles of pulse code modulation (PCM) and adaptive differential pulse code modulation (ADPCM) standards Linear prediction (LP) and use of the linear predictive coding (LPC) model Vector quantization and its applications in speech coding Case studies of practical speech coders from ITU and others The Internet low-bit-rate coder (ILBC) Developed from the authors’ combined teachings, this book also illustrates its contents by providing a real-time implementation of a speech coder on a digital signal processing chip. With its balance of theory and practical coverage, it is ideal for senior-level undergraduate and graduate students in electrical and computer engineering. It is also suitable for engineers and researchers designing or using speech coding systems in their work.

Introduction to Speech CodingSpeech SignalsCharacteristics of Speech SignalsModeling of SpeechSpeech AnalysisSpeech CodingVarieties of Speech CodersMeasuring Speech QualityCommunication Networks and Speech CodingPerformance Issues in Speech Communication SystemsSummary of Speech Coding Standards Fundamentals of DSP for Speech Processing Introduction to LTI Systems Review of Digital Signal Processing Review of Stochastic Signal ProcessingResponse of a Linear System to a Stochastic Process InputWindowing AR Models for Speech Signals, Yule–Walker EquationsShort-Term Frequency (or Fourier) Transform and Cepstrum PeriodogramsSpectral Envelope Determination for Speech SignalsVoiced/Unvoiced Classification of Speech SignalsPitch Period Estimation Methods Sampling TheoryNyquist Sampling Theorem Reconstruction of the Original Signal: Interpolation FiltersPractical ReconstructionAliasing and In-Band DistortionEffect of Sampling Clock JitterSampling and Reconstruction of Random Signals Waveform Coding and QuantizationQuantization Quantizer Performance Evaluation Quantizer Transfer Function Quantizer Performance under No-Overload Conditions Uniform Quantizer Nonuniform Quantizer Logarithmic Companding Segmented Companding LawsITU G.711 µ-Law and A-Law PCM StandardsOptimum Quantization Adaptive Quantization Differential CodingClosed-Loop Differential Quantizer Generalization to Predictive Coding ITU G.726 ADPCM AlgorithmLinear DeltamodulationAdaptive Deltamodulation Linear PredictionProperties of the Autocorrelation Matrix, R 136Relation between Linear Prediction and AR ModelingAugmented Wiener Hopf Equations for Forward PredictionBackward Prediction-Error FilterAugmented Wiener Hopf Equations for Backward PredictionLD Recursion Linear Predictive CodingLinear Predictive CodingLPC-10 Federal StandardIntroduction to CELP-Based Coders Vector Quantization for Speech Coding ApplicationsReview of Scalar QuantizationVector QuantizationLloyd’s Algorithm for Vector Quantizer DesignThe Linde–Buzo–Gray AlgorithmPopular Search Algorithms for VQ Quantizer DesignOther Suboptimal Algorithms for VQ Quantizer DesignApplications in Standards Analysis-by-Synthesis Coding of SpeechCELP AbS StructureCase Study Example: FS 1016 CELP CoderCase Study Example: ITU-T G.729/729A Speech Coder Internet Low-Bit-Rate CoderInternet Low-Bit-Rate Codec .242iLBC’s Encoding Process 245iLBC’s Decoding Process 250iLBC’s PLC Techniques 253iLBC’s Enhancement Techniques 254iLBC’s Synthesis and Postfiltering 257MATLAB’s Signal Processing Blockset iLBC Demo ModelPESQEvolution from PSQM/PSQM + TO PESQPESQ AlgorithmPESQ Applications Signal Processing in VoIP Systems PSTN and VoIP NetworksEffect of Delay on the Perceived Speech QualityLine ECANsAcoustic ECANsJitter BuffersClock SkewPacket Loss Recovery Methods Real-Time DSP Implementation of ITU-T G.729/A Speech CoderITU-T G.729/A Speech Coding StandardTI TMS320C6X DSP ProcessorsTI’s RF and DSP Algorithm StandardG.729/A on RF3 on the TI C6X DSPRunning the RF3 Example on EVMRF3 Resource RequirementsDetails of Our ImplementationMigrating ITU-T G.729/A to RF3 and the EVMOptimizing G.729/A for Real-Time Execution on the EVMReal-Time Performance for Two ChannelsChecking the Test Vectors on the EVMGoing Beyond a Two-Channel Implementation Conclusions and Future Directions for Speech CodingSummaryFuture Directions for Speech Research References Index

Tokunbo Ogunfunmi is a professor in the department of electrical engineering and Director of the Signal Processing Research Lab. (SPRL) at Santa Clara University, California. His research interests include digital adaptive/nonlinear signal processing, speech and video signal processing, artificial neural networks and VLSI design. He has published two books and over 100 refereed journal and conference papers in these and related application areas. Dr. Ogunfunmi has been a consultant to industry and government and a visiting professor at Stanford University and The University of Texas. He is a Senior Member of the Institution of Electrical and Electronic Engineers (IEEE), a Member of Sigma Xi (the Scientific Research Society) , and Member of the American Association for the Advancement of Science (AAAS). He serves as the Chair of the IEEE Signal Processing Society (SPS) Santa Clara Valley Chapter and as a member of several IEEE Technical Committees (TC). He is also a registered professional engineer. Madihally (Sim) Narasimha is currently a Senior Director of Technology at Qualcomm Inc. Prior to joining Qualcomm, he was Vice President of Technology at Ample Communications, where he directed the development of Ethernet physical layer chips. Prior to that, he served in technology leadership roles at several Voice-over-IP (VoIP) startup companies including IP Unity, Realchip Communications, and Empowertel Networks. He also held senior management positions at Symmetricom and Granger Associates (a subsidiary of DSC Communications Corporation), where he was instrumental in bringing many DSP-based telecommunications products to market. Dr. Narasimha is also a Consulting Professor in the Department of Electrical Engineering at Stanford University, Stanford, CA, where he teaches telecommunications courses and performs research in related areas.He is a Fellow of the Institution of Electrical and Electronic Engineers (IEEE).