Code-excited linear prediction

About: Code-excited linear prediction is a research topic. Over the lifetime, 2025 publications have been published within this topic receiving 28633 citations. The topic is also known as: CELP.

Toll-quality digital secraphone

Abstract: This paper describes the design and performance of a secraphone that, when plugged between any conventional telephone set and the public telephone network, protects the speech information travelling through the PSTN. The device has a transparent operating mode that does not alter the signal and a secure mode, accessed upon request of any of the speakers, that encrypts the speech with digital techniques, assuring privacy against unwanted listeners. At the transmission branch, voice is sampled, coded with a CELP scheme at 9600 bps (with a slow mode at 7200 bps), encrypted with a proprietary algorithm and interfaced to the line with a V.32 modem chip set. The keys for encryption are established at the beginning of every transmission. Voice quality is preserved by the coding scheme, specially at the highest transmission rate, and cryptoanalysis techniques are being applied to test the robustness of the encryption.

Method and apparatus for transmitting an encoded speech signal

Abstract: A method and apparatus for processing speech in a wireless communication system uses CELP speech encoded signals. A speech input receives samples of a speech signal and a codebook analysis block for selects an index of a code from at least one of a plurality of codebooks. A weighted synthesis filter is used in the generation of a prediction error between a predicted current sample and a current sample of the speech samples. The index is transmitted to the receiver to enable reconstruction of the speech signal at the receiver.

A 6.6 kb/s CELP speech coder: high performance for GSM half-rate system

Abstract: The paper describes a 6.6 kb/s speech coder with high performance, suitable for the GSM half-rate system. The coder is based on the codebook excited linear prediction (CELP) technique. Line spectrum pair (LSP) representation is introduced. Fast stochastic codebook search procedures based on a linear-shifted Gaussian noise sequence are proposed and a detailed description of gain quantizers and adaptive post-filter is presented. This coder structure also exhibits good performance at a data rate as low as 4.8 kb/s. >

Sparsity in Linear Predictive Coding of Speech

Abstract: This thesis deals with developing improved techniques for speech coding based on the recent developments in sparse signal representation. In particular, this work is motivated by the need to address some of the limitations of the wellknown linear prediction (LP) model currently applied in many modern speech coders. In the first part of the thesis, we provide an overview of Sparse Linear Prediction, a set of speech processing tools created by introducing sparsity constraints into the LP framework. This approach defines predictors that look for a sparse residual rather than a minimum variance one with direct applications to coding but also consistent with the speech production model of voiced speech, where the excitation of the all-pole filter can be modeled as an impulse train, i.e., a sparse sequence. Introducing sparsity in the LP framework will also bring to develop the concept of high-order sparse predictors. These predictors, by modeling efficiently the spectral envelope and the harmonics components with very few coefficients, have direct applications in speech processing, engendering a joint estimation of short-term and long-term predictors. We also give preliminary results of the effectiveness of their application in audio processing. The second part of the thesis deals with introducing sparsity directly in the linear prediction analysis-by-synthesis (LPAS) speech coding paradigm. We first propose a novel near-optimal method to look for a sparse approximate excitation using a compressed sensing formulation. Furthermore, we define a novel re-estimation procedure to adapt the predictor coefficients to the given sparse excitation, balancing the two representations in the context of speech coding. Finally, the advantages of the compact parametric representation of a segment of speech, given by the sparse linear predictors and the use of the reestimation procedure, are analyzed in the context of frame independent coding for speech communications over packet networks.