Persistent Time-Frequency Shrinkage for Audio Denoising

Siedenburg K, Dörfler M. Persistent Time-Frequency Shrinkage for Audio Denoising. J Audio Eng Soc. 2013;61(1/2):29-38. Available from: https://aes.org/publications/elibrary-page/?id=16665

@article{Siedenburg2013_16665,
  author = {Siedenburg, Kai and Dörfler, Monika},
  title = {{Persistent Time-Frequency Shrinkage for Audio Denoising}},
  journal = {Journal of the Audio Engineering Society},
  volume = {61},
  number = {1/2},
  pages = {29--38},
  year = {2013},
  month = jan,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16665}
}

TY  - JOUR
TI  - Persistent Time-Frequency Shrinkage for Audio Denoising
AU  - Siedenburg, Kai
AU  - Dörfler, Monika
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 61
IS  - 1/2
SP  - 29
EP  - 38
PY  - 2013
DA  - 2013/01/06
UR  - https://aes.org/publications/elibrary-page/?id=16665
PB  - Audio Engineering Society
LA  - en
AB  - In many audio processing applications, signals are represented by linear combinations of basis functions (such as with windowed Fourier transforms) that are collected in so-called dictionaries. These are considered well adapted to a particular class of signals if they lead to sparse representations, meaning only a small number of basis functions are required for good approximation of signals. Most natural signals have strong inherent structures, such as harmonics and transients, a fact that can be used for adapting audio processing algorithms. This paper considers the audio-denoising problem from the perspective of structured sparse representation. A generalized thresholding scheme is presented from which simple audio-denoising operators are derived. They perform equally well compared to state-of-the-art methods while featuring significantly less computational costs.
ER  -