Abstract
The introduction of a zoom technique which combines the advantages of subband decomposition and linear prediction for spectral-analysis applications is discussed. The efficiency of the technique is tested under different transform lengths, different linear prediction orders, different frequency components, and different SNR. As the number of subbands increases, the spectral resolution improves accordingly without increasing the transform length. The technique shows an equivalent or even superior performance, with the advantage of its reduced complexity.
| Original language | English |
|---|---|
| Pages (from-to) | 2195-2206 |
| Number of pages | 12 |
| Journal | Signal Processing |
| Volume | 84 |
| Issue number | 11 SPEC. ISS. |
| DOIs | |
| Publication status | Published - Nov 2004 |
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ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
Cite this
Parametric modelling of decomposed subbands : Resolution improvement and applications for narrow-band signals. / Hossen, Abdulnasir; Heute, Ulrich.
In: Signal Processing, Vol. 84, No. 11 SPEC. ISS., 11.2004, p. 2195-2206.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Parametric modelling of decomposed subbands
T2 - Resolution improvement and applications for narrow-band signals
AU - Hossen, Abdulnasir
AU - Heute, Ulrich
PY - 2004/11
Y1 - 2004/11
N2 - The introduction of a zoom technique which combines the advantages of subband decomposition and linear prediction for spectral-analysis applications is discussed. The efficiency of the technique is tested under different transform lengths, different linear prediction orders, different frequency components, and different SNR. As the number of subbands increases, the spectral resolution improves accordingly without increasing the transform length. The technique shows an equivalent or even superior performance, with the advantage of its reduced complexity.
AB - The introduction of a zoom technique which combines the advantages of subband decomposition and linear prediction for spectral-analysis applications is discussed. The efficiency of the technique is tested under different transform lengths, different linear prediction orders, different frequency components, and different SNR. As the number of subbands increases, the spectral resolution improves accordingly without increasing the transform length. The technique shows an equivalent or even superior performance, with the advantage of its reduced complexity.
UR - http://www.scopus.com/inward/record.url?scp=4944252487&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=4944252487&partnerID=8YFLogxK
U2 - 10.1016/j.sigpro.2004.07.021
DO - 10.1016/j.sigpro.2004.07.021
M3 - Article
AN - SCOPUS:4944252487
VL - 84
SP - 2195
EP - 2206
JO - Signal Processing
JF - Signal Processing
SN - 0165-1684
IS - 11 SPEC. ISS.
ER -