A novel extension of the SB-FFT

Sub-segment inverse fast Fourier transform (SS-IFFT) with different applications

Abdulnasir Hossen, Ulrich Heute

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper we present a new fast approximate inverse FFT for short-time signal applications. This approach is derived from the sub-band FFT (SB-FFT) and it is called Sub-Segment IFFT (SS-IFFT). SS-IFFT uses the idea of decomposing the input signal into two segments (early and late) according to their order of occurence in time. An approximation can be done by implementing the IFFT of one of the two-segments according to a pre-known information about the time-domain characteristics of the signal. Such an approximation leads to fast computation at the cost of less accuracy. Both the reduction in complexity and the approximation errors of the new algorithm are investigated in this paper. The SS-IFFT has an adaptive capability like the forward SB-FFT. The idea of SS-IFFT is extended also to the two dimensional case. The algorithm is also tested by using different filters other than the Hadamard filters used in the SB-FFT. Different applications of the new technique are included in speech analysis, echo detection, FIR filter design, and ECG compression.

Original languageEnglish
Pages (from-to)361-375
Number of pages15
JournalJournal of Computational Methods in Sciences and Engineering
Volume13
Issue number3-4
DOIs
Publication statusPublished - 2013

Fingerprint

Fast Fourier transform
Fast Fourier transforms
Speech Analysis
Filter
FIR Filter
Approximate Inverse
Filter Design
Approximation Error
Approximation
Speech analysis
FIR filters
Time Domain
Electrocardiography
Compression

Keywords

  • approximation errors
  • complexity
  • different wavelets filters
  • ECG compression
  • echo detection
  • Fast algorithms
  • FIR filters design
  • SB-FFT
  • speech analysis
  • SS-IFFT

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Mathematics
  • Engineering(all)

Cite this

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title = "A novel extension of the SB-FFT: Sub-segment inverse fast Fourier transform (SS-IFFT) with different applications",
abstract = "In this paper we present a new fast approximate inverse FFT for short-time signal applications. This approach is derived from the sub-band FFT (SB-FFT) and it is called Sub-Segment IFFT (SS-IFFT). SS-IFFT uses the idea of decomposing the input signal into two segments (early and late) according to their order of occurence in time. An approximation can be done by implementing the IFFT of one of the two-segments according to a pre-known information about the time-domain characteristics of the signal. Such an approximation leads to fast computation at the cost of less accuracy. Both the reduction in complexity and the approximation errors of the new algorithm are investigated in this paper. The SS-IFFT has an adaptive capability like the forward SB-FFT. The idea of SS-IFFT is extended also to the two dimensional case. The algorithm is also tested by using different filters other than the Hadamard filters used in the SB-FFT. Different applications of the new technique are included in speech analysis, echo detection, FIR filter design, and ECG compression.",
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AB - In this paper we present a new fast approximate inverse FFT for short-time signal applications. This approach is derived from the sub-band FFT (SB-FFT) and it is called Sub-Segment IFFT (SS-IFFT). SS-IFFT uses the idea of decomposing the input signal into two segments (early and late) according to their order of occurence in time. An approximation can be done by implementing the IFFT of one of the two-segments according to a pre-known information about the time-domain characteristics of the signal. Such an approximation leads to fast computation at the cost of less accuracy. Both the reduction in complexity and the approximation errors of the new algorithm are investigated in this paper. The SS-IFFT has an adaptive capability like the forward SB-FFT. The idea of SS-IFFT is extended also to the two dimensional case. The algorithm is also tested by using different filters other than the Hadamard filters used in the SB-FFT. Different applications of the new technique are included in speech analysis, echo detection, FIR filter design, and ECG compression.

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