Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

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 in Web of Science"
}