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Bird song scene analysis using a spatial-cue-based probabilistic model
Bird song scene analysis using a spatial-cue-based probabilistic model
- Source :
- Journal of Robotics and Mechatronics. 29(1):236-246
- Publication Year :
- 2017
-
Abstract
- [abstFig src='/00290001/22.jpg' width='300' text='Spatial-cue-based probabilistic model' ] This paper addresses bird song scene analysis based on semi-automatic annotation. Research in animal behavior, especially in birds, would be aided by automated or semi-automated systems that can localize sounds, measure their timing, and identify their sources. This is difficult to achieve in real environments, in which several birds at different locations may be singing at the same time. Analysis of recordings from the wild has usually required manual annotation. These annotations may be inaccurate or inconsistent, as they may vary within and between observers. Here we suggest a system that uses automated methods from robot audition, including sound source detection, localization, separation and identification. In robot audition, these technologies are assessed separately, but combining them has often led to poor performance in natural setting. We propose a new Spatial-Cue-Based Probabilistic Model (SCBPM) for their integration focusing on spatial information. A second problem has been that supervised machine learning methods usually require a pre-trained model, which may need a large training set of annotated labels. We have employed a semi-automatic annotation approach, in which a semi-supervised training method is deduced for a new model. This method requires much less pre-annotation. Preliminary experiments with recordings of bird songs from the wild revealed that our system outperformed the identification accuracy of a method based on conventional robot audition.**This paper is an extension of a proceeding of IROS2015.
- Subjects :
- Scene analysis
General Computer Science
business.industry
Computer science
Speech recognition
Statistical model
02 engineering and technology
030507 speech-language pathology & audiology
03 medical and health sciences
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer vision
Artificial intelligence
Electrical and Electronic Engineering
0305 other medical science
business
Subjects
Details
- Language :
- English
- Volume :
- 29
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Journal of Robotics and Mechatronics
- Accession number :
- edsair.doi.dedup.....8f90cce970ba45f14580aafb6eafd2fb