Journal Article10.1109/5.326413
Speech coding: a tutorial review
Andreas Spanias
- 01 Oct 1994
- Vol. 82, Iss: 10, pp 1541-1582
484
TL;DR: The objective of this paper is to provide a tutorial overview of speech coding methodologies with emphasis on those algorithms that are part of the recent low-rate standards for cellular communications.
read more
Abstract: The past decade has witnessed substantial progress towards the application of low-rate speech coders to civilian and military communications as well as computer-related voice applications. Central to this progress has been the development of new speech coders capable of producing high-quality speech at low data rates. Most of these coders incorporate mechanisms to: represent the spectral properties of speech, provide for speech waveform matching, and "optimize" the coder's performance for the human ear. A number of these coders have already been adopted in national and international cellular telephony standards. The objective of this paper is to provide a tutorial overview of speech coding methodologies with emphasis on those algorithms that are part of the recent low-rate standards for cellular communications. Although the emphasis is on the new low-rate coders, we attempt to provide a comprehensive survey by covering some of the traditional methodologies as well. We feel that this approach will not only point out key references but will also provide valuable background to the beginner. The paper starts with a historical perspective and continues with a brief discussion on the speech properties and performance measures. We then proceed with descriptions of waveform coders, sinusoidal transform coders, linear predictive vocoders, and analysis-by-synthesis linear predictive coders. Finally, we present concluding remarks followed by a discussion of opportunities for future research. >
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Book
Bayesian Reasoning and Machine Learning
David Barber
- 12 Mar 2012
TL;DR: Comprehensive and coherent, this hands-on text develops everything from basic reasoning to advanced techniques within the framework of graphical models, and develops analytical and problem-solving skills that equip them for the real world.
Perceptual coding of digital audio
T. Painter,Andreas Spanias +1 more
- 01 Apr 2000
TL;DR: This paper reviews methodologies that achieve perceptually transparent coding of FM- and CD-quality audio signals, including algorithms that manipulate transform components, subband signal decompositions, sinusoidal signal components, and linear prediction parameters, as well as hybrid algorithms that make use of more than one signal model.
Swipe: a sawtooth waveform inspired pitch estimator for speech and music
John G. Harris,Arturo Camacho +1 more
TL;DR: SWIPE('), a variation of SWIPE, utilizes only the first and prime harmonics of the signal, which significantly reduces subharmonic errors commonly found in other pitch estimation algorithms.
A speech/music discriminator based on RMS and zero-crossings
TL;DR: The goal was to first develop a system for segmentation of the audio signal, and then classification into one of two main categories: speech or music, and results show that efficiency is exceptionally good, without sacrificing performance.
Lossy source coding
Toby Berger,Jerry D. Gibson +1 more
TL;DR: This work chronicles the development of rate-distortion theory and provides an overview of its influence on the practice of lossy source coding.
243
References
A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
74.4K
Least squares quantization in PCM
TL;DR: In this article, the authors derived necessary conditions for any finite number of quanta and associated quantization intervals of an optimum finite quantization scheme to achieve minimum average quantization noise power.
Least Squares Quantization in PCM
S. P. Lloyd
- 01 Jan 1982
TL;DR: The corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy.
9.6K
An Algorithm for Vector Quantizer Design
Y. Linde,A. Buzo,Robert M. Gray +2 more
TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
•Book
Vector Quantization and Signal Compression
Allen Gersho,Robert M. Gray +1 more
- 01 Jan 1991
TL;DR: The author explains the design and implementation of the Levinson-Durbin Algorithm, which automates the very labor-intensive and therefore time-heavy and expensive process of designing and implementing a Quantizer.
8K
Related Papers (5)
T. Painter,Andreas Spanias +1 more
- 01 Apr 2000
John Makhoul
- 01 Apr 1975
Lawrence R. Rabiner,Ronald W. Schafer +1 more
- 05 Sep 1978