Name
Abstract | ||
|---|---|---|
A bird phrase segmentation method using entropy-based change point detection is proposed. Spectrograms of bird calls are usually sparse while the background noise is relatively white. Therefore, considering the entropy of a sliding time-frequency block on the spectrogram, the entropy dips when detecting a signal and rises when the signal ends. Rather than applying a hard threshold on the entropy to determine the beginning and ending of a signal, a Bayesian change point detection is used to detect the statistical changes in the entropy sequence. Tests on a database of Cassin's Vireo (Vireo cassinii), our proposed segmentation method with spectral subtraction or a novel spectral whitening method as the front-end generates more accurate time labels, lower the false alarm rate than the conventional time-domain energy detection method and achieves high phrase classification rate. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ChinaSIP.2013.6625329 | ChinaSIP |
Field | DocType | Citations |
Background noise,Change detection,Vireo,Detection theory,Pattern recognition,Segmentation,Computer science,Spectrogram,Phrase,Artificial intelligence,Constant false alarm rate | Conference | 1 |
PageRank | References | Authors |
0.40 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ni-Chun Wang | 1 | 5 | 0.85 |
| Ralph E. Hudson | 2 | 1 | 0.40 |
| Lee Ngee Tan | 3 | 43 | 5.00 |
| Charles E. Taylor | 4 | 1 | 0.40 |
| Abeer Alwan | 5 | 729 | 88.19 |
| Rung Yao | 6 | 1 | 0.40 |