Files and links (1)
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
Analysis Acoustic Features for Acoustic Scene Classification and Score fusion of multi-classification systems applied to DCASE 2016 challenge
arXiv (Cornell University)
13 Jul 2018
Abstract
Park, S., Mun, S., Lee, Y., and Ko, H. (2016). Score fusion of
classification systems for acoustic scene classification. IEEE AASP Challenge
on Detection and Classification of Acoustic Scenes and Events (DCASE) This paper describes an acoustic scene classification method which achieved
the 4th ranking result in the IEEE AASP challenge of Detection and
Classification of Acoustic Scenes and Events 2016. In order to accomplish the
ensuing task, several methods are explored in three aspects: feature
extraction, feature transformation, and score fusion for final decision. In the
part of feature extraction, several features are investigated for effective
acoustic scene classification. For resolving the issue that the same sound can
be heard in different places, a feature transformation is applied for better
separation for classification. From these, several systems based on different
feature sets are devised for classification. The final result is determined by
fusing the individual systems. The method is demonstrated and validated by the
experiment conducted using the Challenge database.
Metrics
8 Record Views
Details
- Title
- Analysis Acoustic Features for Acoustic Scene Classification and Score fusion of multi-classification systems applied to DCASE 2016 challenge
- Creators
- Sangwook ParkSeongkyu MunYounglo LeeDavid K HanHanseok Ko
- Publication Details
- arXiv (Cornell University)
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Other Identifier
- 991021931081404721