Spectral analysis of audio signal using complex discrete cosine transform

The main method of spectral analysis of the sound broadcasting signal (SBS) remains the fast Fourier transform. One of its disadvantages is insufficient resolution, which does not allow to estimate closely spaced spectral components of the SBS, as well as a sufficiently rich spectrum of side lobes deter-mined by the window function used. The need to use window functions also leads to the need to work with overlaps of the analyzed sequence. The resolution of the discrete cosine transform (DCT) is twice as high, but its use was constrained by a wide range of side lobes. The advantage of DCT is also the absence of the need to use window functions, and, accordingly, overlaps. The authors propose an algorithm for a complex DCT with side lobe compensation and in-creased accuracy of determining the parameters of the spectral components due to multiple transposition of the SB spectrum. The algorithm allows to bring the resolution and accuracy of spectral analysis closer to the parameters of the human peripheral auditory analyzer. The developed algorithm of complex cosine discrete transform provides formation of an estimate similar to DFT but with twice higher resolution and lower level of side lobes due to use of active window functions. The obtained results showed the efficiency of the developed algorithm and the prospects of its use in analysis of the spectrum of the audio signal, its coding and processing.

Authors

Publication Details

Journal
Electromagnetic Waves and Electronic Systems
Published
2025-01-01
DOI
https://doi.org/10.18127/j5604128-202504-01
Primary Topic
Structural Health Monitoring Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spectral analysis of audio signal using complex discrete cosine transform

P. S. Sapronov, Т. В. Чернышева, K. V. Orlov, Oliver Popov
Electromagnetic Waves and Electronic Systems
Structural Health Monitoring Techniques
article

Spectral analysis of audio signal using complex discrete cosine transform

P. S. Sapronov, Т. В. Чернышева, K. V. Orlov, Oliver Popov
article en

Abstract

The main method of spectral analysis of the sound broadcasting signal (SBS) remains the fast Fourier transform. One of its disadvantages is insufficient resolution, which does not allow to estimate closely spaced spectral components of the SBS, as well as a sufficiently rich spectrum of side lobes deter-mined by the window function used. The need to use window functions also leads to the need to work with overlaps of the analyzed sequence. The resolution of the discrete cosine transform (DCT) is twice as high, but its use was constrained by a wide range of side lobes. The advantage of DCT is also the absence of the need to use window functions, and, accordingly, overlaps. The authors propose an algorithm for a complex DCT with side lobe compensation and in-creased accuracy of determining the parameters of the spectral components due to multiple transposition of the SB spectrum. The algorithm allows to bring the resolution and accuracy of spectral analysis closer to the parameters of the human peripheral auditory analyzer. The developed algorithm of complex cosine discrete transform provides formation of an estimate similar to DFT but with twice higher resolution and lower level of side lobes due to use of active window functions. The obtained results showed the efficiency of the developed algorithm and the prospects of its use in analysis of the spectrum of the audio signal, its coding and processing.

Electromagnetic Waves and Electronic SystemsVol. 30(4)
Openalex Percentile: Top 73%
Structural Health Monitoring Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.