Digital Signal Processing

Contains a research summary and reports on fifteen research projects.National Science Foundation FellowshipJoint Services Electronics Program (Contract DAAG29-78-C-0020)National Science Foundation (Grant ENG76-24117)U.S. Navy - Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ENG76-24117)Schlumberger-Doll Research Center FellowshipHertz Foundation FellowshipNational Aeronautics and Space Administration (Grant NSG-5157)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0196

Digital Signal Processing

Contains research objectives and reports on sixteen research projects.U.S. Navy - Office of Naval Research (Contract N00014-75-C-0852)National Science Foundation FellowshipNational Science Foundation (Grant ENG76-24117)U.S. Navy - Office of Naval Research (Contract N00014-77-C-0257)U.S. Air Force (Contract F19628-80-C-0002)U.S. Navy - Office of Naval Research (Contract N00014-75-C-0951)Schlumberger-Doll Research Center FellowshipHertz Foundation FellowshipGovernment of Pakistan ScholarshipU.S. Navy - Office of Naval Research (Contract N00014-77-C-0196

Novel Pitch Detection Algorithm With Application to Speech Coding

This thesis introduces a novel method for accurate pitch detection and speech segmentation, named Multi-feature, Autocorrelation (ACR) and Wavelet Technique (MAWT). MAWT uses feature extraction, and ACR applied on Linear Predictive Coding (LPC) residuals, with a wavelet-based refinement step. MAWT opens the way for a unique approach to modeling: although speech is divided into segments, the success of voicing decisions is not crucial. Experiments demonstrate the superiority of MAWT in pitch period detection accuracy over existing methods, and illustrate its advantages for speech segmentation. These advantages are more pronounced for gain-varying and transitional speech, and under noisy conditions

A review of differentiable digital signal processing for music and speech synthesis

The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music and speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably, which is further supported by a web book containing practical advice on differentiable synthesiser programming (https://intro2ddsp.github.io/). Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research

Phase estimation with application to speech analysis-synthesis

Originally published as thesis (Dept. of Electrical Engineering and Computer Science, Sc.D., 1979).Bibliography: p. 133-135.Supported in part by the Advanced Research Projects Agency (monitored by ONR) under Contract N00014-75-C-0951 NR 409-328Thomas F. Quatieri, Jr

The low bit-rate coding of speech signals

Imperial Users onl

Speech coding

Speech is the predominant means of communication between human beings and since the invention of the telephone by Alexander Graham Bell in 1876, speech services have remained to be the core service in almost all telecommunication systems. Original analog methods of telephony had the disadvantage of speech signal getting corrupted by noise, cross-talk and distortion Long haul transmissions which use repeaters to compensate for the loss in signal strength on transmission links also increase the associated noise and distortion. On the other hand digital transmission is relatively immune to noise, cross-talk and distortion primarily because of the capability to faithfully regenerate digital signal at each repeater purely based on a binary decision. Hence end-to-end performance of the digital link essentially becomes independent of the length and operating frequency bands of the link Hence from a transmission point of view digital transmission has been the preferred approach due to its higher immunity to noise. The need to carry digital speech became extremely important from a service provision point of view as well. Modem requirements have introduced the need for robust, flexible and secure services that can carry a multitude of signal types (such as voice, data and video) without a fundamental change in infrastructure. Such a requirement could not have been easily met without the advent of digital transmission systems, thereby requiring speech to be coded digitally. The term Speech Coding is often referred to techniques that represent or code speech signals either directly as a waveform or as a set of parameters by analyzing the speech signal. In either case, the codes are transmitted to the distant end where speech is reconstructed or synthesized using the received set of codes. A more generic term that is applicable to these techniques that is often interchangeably used with speech coding is the term voice coding. This term is more generic in the sense that the coding techniques are equally applicable to any voice signal whether or not it carries any intelligible information, as the term speech implies. Other terms that are commonly used are speech compression and voice compression since the fundamental idea behind speech coding is to reduce (compress) the transmission rate (or equivalently the bandwidth) And/or reduce storage requirements In this document the terms speech and voice shall be used interchangeably