Further Developments of the Online Sound Restoration System for Digital Library Applications

Cichowski, Janusz; Kupryjanow, Adam; Czyżewski, Andrzej

doi:10.1007/978-3-319-04714-0_14

Cited by 1 publication

(2 citation statements)

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“…The algorithms for sound restoration make a well-established domain of audio acoustics. Behind the youarchive.net system operation many implemented algorithms stand, originated from the ones published earlier by the authors of this paper [1][2][3] or by other authors, e. g. [4][5][6] [10][11]. However, some original ideas are also utilized, bringing a possibility to get a better functioning of the created service.…”

Section: Algorithmic Background Outlinementioning

confidence: 99%

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Online sound restoration system for digital library applications

Czyżewski¹,

Ciarkowski²,

Kostek³

et al. 2014

Proceedings of Meetings on Acoustics

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ac@pg.gda.pl Audio signal processing algorithms were introduced to the new online non-commercial service for audio restoration intended to enhance the con-tent of digitized audio repositories. Missing or distorted audio samples are predicted using neural networks and a specific implementation of the Jann-sen interpolation method based on the autoregressive model (AR) combined with the iterative restoring of missing signal samples. Since the distortion prediction and compensations algorithms are computationally complex, an implementation which uses parallel computing has been proposed. Many archival recordings are at the same time clipped and affected by wideband noise. To restore those recordings, the algorithm based on the concatenation of signal clipping reduction and spectral expansion was proposed. The clip-ping reduction algorithm uses an intelligent interpolation to replace dis-torted samples with the predicted ones based on learning algorithms. Next, spectral expansion is performed in order to reduce the overall level of noise. The online service has been extended with some copyright protection mech-anisms. Immunity of watermarks to the sound restoration is discussed with regards to low-level music feature vectors embedded as watermarks. Then, algorithmic issues pertaining watermarking techniques are briefly recalled. The architecture of the designed system is presented.

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Section: Algorithmic Background Outlinementioning

confidence: 99%

“…The watermark embedding process is shown in Fig. 9 and it was explained very thoroughly in earlier publications by the Authors [3][12] [13]. The parameter α represents watermarking strength.…”

Section: Embedding and Extracting Watermarks For Copyrights Protectionmentioning

confidence: 99%