Paper Info
Title
Dictionary Learning for Bioacoustics Monitoring with Applications to Species Classification.
Abstract
This paper deals with the application of the convolutive version of dictionary learning to analyze in-situ audio recordings for bio-acoustics monitoring. We propose an efficient approach for learning and using a sparse convolutive model to represent a collection of spectrograms. In this approach, we identify repeated bioacoustics patterns, e.g., bird syllables, as words and represent new spectrograms using these words. Moreover, we propose a supervised dictionary learning approach in the multiple-label setting to support multi-label classification of unlabeled spectrograms. Our approach relies on a random projection for reduced computational complexity. As a consequence, the non-negativity requirement on the dictionary words is relaxed. Furthermore, 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 successfully apply the approach to spectrogram denoising and species classification.
Year
DOI
Venue
2018
https://doi.org/10.1007/s11265-016-1155-0
Signal Processing Systems
Keywords
Field
DocType
Dictionary learning,Random matrix projection,Classification
Noise reduction,Random projection,K-SVD,Computer science,Bioacoustics,Artificial intelligence,Dictionary learning,Pattern recognition,Spectrogram,Speech recognition,Syllable,Machine learning,Computational complexity theory
Journal
Volume
Issue
ISSN
90
2
1939-8018
Citations 
PageRank 
References 
0
0.34
17
Authors
4
Name
Order
Citations
PageRank
José Francisco Ruiz-Muñoz1112.91
Zeyu You262.83
Raviv Raich343258.13
Xiaoli Z. Fern4112363.73