Name
Abstract | ||
|---|---|---|
In this paper, a supervised algorithm is proposed for the identification and segmentation of bird calls with K-means clustering using features learnt by matrix factorization. Singular value decomposition is applied on pooled time-frequency vocalization in a class-wise manner to learn a class-specific feature representation. These representations show discriminative behavior even when unseen classes are represented. By combining the proposed feature representation with K-means clustering, we are able to effectively cluster and segment bird calls from multiple species, which are present in an input recording. Experimental results are provided on a small dataset of birdsong. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICIINFS.2018.8721418 | 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS) |
Keywords | DocType | ISSN |
Birds,Time-frequency analysis,Mel frequency cepstral coefficient,Spectrogram,Clustering algorithms,Dictionaries,Testing | Conference | 2164-7011 |
ISBN | Citations | PageRank |
978-1-5386-8492-4 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Harshita Seth | 1 | 0 | 0.68 |
| Rhythm Bhatia | 2 | 0 | 0.68 |
| Padmanabhan Rajan | 3 | 0 | 1.35 |