Publication detail

Toward High-Quality Real-Time Signal Reconstruction from STFT Magnitude

PRŮŠA, Z. RAJMIC, P.

Original Title

Toward High-Quality Real-Time Signal Reconstruction from STFT Magnitude

English Title

Toward High-Quality Real-Time Signal Reconstruction from STFT Magnitude

Type

journal article

Language

en

Original Abstract

An efficient algorithm for real-time signal reconstruction from the magnitude of the short-time Fourier transform (STFT) is introduced. The proposed approach combines the strengths of two previously published algorithms: the Real-Time Phase Gradient Heap Integration (RTPGHI) and the Gnann and Spiertz’s Real-Time Iterative Spectrogram Inversion with Look-Ahead (GSRTISI-LA). An extensive comparison with the state-of-the-art algorithms in a reproducible manner is presented.

English abstract

An efficient algorithm for real-time signal reconstruction from the magnitude of the short-time Fourier transform (STFT) is introduced. The proposed approach combines the strengths of two previously published algorithms: the Real-Time Phase Gradient Heap Integration (RTPGHI) and the Gnann and Spiertz’s Real-Time Iterative Spectrogram Inversion with Look-Ahead (GSRTISI-LA). An extensive comparison with the state-of-the-art algorithms in a reproducible manner is presented.

Keywords

Time-frequency; short-time Fourier transform; STFT; phase reconstruction; real-time; spectrogram

Released

27.04.2017

Pages from

892

Pages to

896

Pages count

5

URL

BibTex


@article{BUT135233,
  author="Zdeněk {Průša} and Pavel {Rajmic}",
  title="Toward High-Quality Real-Time Signal Reconstruction from STFT Magnitude",
  annote="An efficient algorithm for real-time signal reconstruction from the magnitude of the short-time Fourier transform (STFT) is introduced. The proposed approach combines the strengths of two previously published algorithms: the Real-Time Phase Gradient Heap Integration (RTPGHI) and the Gnann and Spiertz’s Real-Time Iterative Spectrogram Inversion with Look-Ahead (GSRTISI-LA). An extensive comparison with the state-of-the-art algorithms in a reproducible manner is presented.",
  chapter="135233",
  doi="10.1109/LSP.2017.2696970",
  howpublished="online",
  number="6",
  volume="24",
  year="2017",
  month="april",
  pages="892-- 896",
  type="journal article"
}