VLDB 2026 Research / reviewers in the wild / expert
S. S. Yedlapalli
dblp:25/7145
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Audio and music processing
linear prediction |
0.1 | 1 | 2005 | 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 |
0.1 | 1 | 2005 | 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 |
0.1 | 1 | 2005 | 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 |
0.1 | 1 | 2005 | 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 |
0.1 | 1 | 2005 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2005 | Transforming Real Linear Prediction Coefficients to Line Spectral Representations With a Real FFTabstractThis 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 |