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S. S. Yedlapalli

dblp:25/7145 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Audio and music processing · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Audio and music processing
linear prediction
0.112005
Transforming Real Linear Prediction Coefficients to Line Spectral Representations With a Real FFT · IEEE Trans. Speech Audio Process. 2005
Audio and music processing › speech coding
line-spectral frequencies
0.112005
Transforming Real Linear Prediction Coefficients to Line Spectral Representations With a Real FFT · IEEE Trans. Speech Audio Process. 2005
Audio and music processing
speech analysis
0.112005
Transforming Real Linear Prediction Coefficients to Line Spectral Representations With a Real FFT · IEEE Trans. Speech Audio Process. 2005
Audio and music processing
speech coding
0.112005
Transforming Real Linear Prediction Coefficients to Line Spectral Representations With a Real FFT · IEEE Trans. Speech Audio Process. 2005
Audio and music processing
speech processing
0.112005
Transforming Real Linear Prediction Coefficients to Line Spectral Representations With a Real FFT · IEEE Trans. Speech Audio Process. 2005

Methods — techniques the papers use, named apart from their topics

plus-minus algorithm · 0.1fixed-point implementation · 0.1fast fourier transform · 0.1
YearPublicationVenuePosition
2005 Transforming Real Linear Prediction Coefficients to Line Spectral Representations With a Real FFT
abstract
This paper describes a novel algorithm for transforming linear prediction coefficients (LPCs) to line spectral frequencies (LSFs) and line spectral pairs (LSPs) used by most of the speech processing applications. The symmetric and antisymmetric polynomials (SAPS) for LSP/LSFs, corresponding to the LPC polynomial, are first multiplexed into a single real sequence. The required samples of SAPS correspond to the DFT of the obtained real sequence. The proposed algorithm is referred as PMLS as it is based on the Plus Minus (PM) algorithm an FFT which efficiently computes the DFT of a real sequence for the positive frequency interval only. The samples of the SAPS are efficiently utilized for the computation of a single parameter which is used for computation of LSF and LSP independently with some interpolation principles. This interpolation exploits the available samples of SAPS and does not require their samples at finer resolution. The efficiency of the PMLS is illustrated with the help of some examples. Some guidelines for an optimal implementation of PMLS on fixed point digital signal processors (DSPs) are also presented.
S. S. Yedlapalli
IEEE Trans. Speech Audio Process.1