Name
Abstract | ||
|---|---|---|
Dictionary learning of spectrograms consists of detecting their fundamental spectra-temporal patterns and their associated activation signals. In this paper, we propose an efficient convolutive dictionary learning approach for analyzing repetitive bioacoustics patterns from a collection of audio recordings. Our method is inspired by the convolutive non-negative matrix factorization (CNMF) model. The proposed approach relies on random projection for reduced computational complexity. As a consequence, the non-negativity requirement on the dictionary words is relaxed. Moreover, the proposed approach is well-suited for a collection of discontinuous spectrograms. We evaluate our approach on synthetic examples and on two real datasets consisting of multiple birds audio recordings. Bird syllable dictionary learning from a real-world dataset is demonstrated. Additionally, we apply the approach for spectrogram denoising in the presence of rain noise artifacts. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MLSP.2015.7324357 | 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | Field | DocType |
Unsupervised Dictionary Learning,Random Matrix Projection | Noise reduction,Random projection,Computer science,Bioacoustics,Artificial intelligence,Dictionary learning,Pattern recognition,Spectrogram,Matrix decomposition,Speech recognition,Syllable,Machine learning,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
1551-2541 | 2 | 0.37 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Francisco Ruiz-Muñoz | 1 | 11 | 2.91 |
| Zeyu You | 2 | 6 | 2.83 |
| Raviv Raich | 3 | 432 | 58.13 |
| Xiaoli Z. Fern | 4 | 1123 | 63.73 |