Name
Abstract | ||
|---|---|---|
In this paper, we describe an unsupervised method to segment birdcalls from the background in bioacoustic recordings. The method utilizes information derived from both source features as well as system features. Three types of source features are extracted from the linear prediction residual signal, and Mel frequency cepstral coefficients are extracted from the system features. The source features are used to generate automatic labels, which are then used to train acoustic models for distinguishing birdcall frames from the background. In the context of a technique proposed earlier, our study demonstrates the improvements brought about by the inclusion of additional source features. |
Year | Venue | Field |
|---|---|---|
2017 | National Conference on Communications NCC | Residual,Mel-frequency cepstrum,Pattern recognition,Computer science,Bioacoustics,Speech recognition,Linear prediction,Feature extraction,Activity detection,Harmonic analysis,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anshul Thakur | 1 | 1 | 4.41 |
| Padmanabhan Rajan | 2 | 22 | 7.63 |