Details

Autor(en) / Beteiligte

• Tjahja, Teresa V.
• Fern, Xiaoli Z.
• Raich, Raviv
• Pham, Anh T.

Titel

Supervised hierarchical segmentation for bird song recording

Ist Teil von

• 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015, p.763-767

Ort / Verlag

IEEE

Erscheinungsjahr

2015

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• A common framework of identifying bird species from audio recordings involves detecting bird song segments, which will be subsequently input to a classifier. In-field recordings are contaminated with various environmental noise. For such recordings, supervised segmentation has been observed to outperform unsupervised energy-based approaches. Prior supervised segmentation work considers only pixel-level predictions and ignores the supervision provided at the segment-level. We propose a hierarchical approach that learns to isolate bird song syllables based on both pixel-level and segment-level information. Experimental results suggest that our method outperforms an existing supervised method that learns only from pixel-level supervision.