This page gathers sound examples* processed with some of the methods studied in the paper « Sparsity-based audio declipping methods: selected overview, new algorithms, and large-scale evaluation« .
The software used to reproduce these results is available under the BSD-3-Clause License (https://opensource.org/licenses/BSD-3-Clause).
Some of the items are extracted from the subjective audio quality experiment reported in the manuscript cited above.
Input SDR: 1dB | Clipped | Adaptive Interpolation [1] | Social Sparsity [2] | A-SPADE [3] | Plain Sparse | Plain Cosparse | Adaptive Social Sparse | Adaptive Social Cosparse | Clean |
---|---|---|---|---|---|---|---|---|---|
Pop | |||||||||
Jazz | |||||||||
Chamber | |||||||||
Orchestra | |||||||||
Vocals |
Input SDR: 3dB | Clipped | Adaptive Interpolation [1] | Social Sparsity [2] | A-SPADE [3] | Plain Sparse | Plain Cosparse | Adaptive Social Sparse | Adaptive Social Cosparse | Clean |
---|---|---|---|---|---|---|---|---|---|
Pop | |||||||||
Jazz | |||||||||
Chamber | |||||||||
Orchestra | |||||||||
Vocals |
Input SDR: 5dB | Clipped | Adaptive Interpolation [1] | Social Sparsity [2] | A-SPADE [3] | Plain Sparse | Plain Cosparse | Adaptive Social Sparse | Adaptive Social Cosparse | Clean |
---|---|---|---|---|---|---|---|---|---|
Pop | |||||||||
Jazz | |||||||||
Chamber | |||||||||
Orchestra | |||||||||
Vocals |
Input SDR: 10dB | Clipped | Adaptive Interpolation [1] | Social Sparsity [2] | A-SPADE [3] | Plain Sparse | Plain Cosparse | Adaptive Social Sparse | Adaptive Social Cosparse | Clean |
---|---|---|---|---|---|---|---|---|---|
Pop | |||||||||
Jazz | |||||||||
Chamber | |||||||||
Orchestra | |||||||||
Vocals |
*The examples processed here are extracted from the RWC Database.
M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka: RWC Music Database: Popular, Classical, and Jazz Music Databases In Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR 2002), 2002.
References
[1]: Janssen, A. J. E. M., Veldhuis, R., & Vries, L. (1986). Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes. IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(2), 317-330.
[2]: Siedenburg, K., Kowalski, M., & Dörfler, M. (2014, May). Audio declipping with social sparsity. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1577-1581). IEEE.
[3]: Kitić, S., Bertin, N., & Gribonval, R. (2015, August). Sparsity and cosparsity for audio declipping: a flexible non-convex approach. In International Conference on Latent Variable Analysis and Signal Separation (pp. 243-250). Springer, Cham.