Soft-computing audio classification as a pre-processor for automated content descriptor generation

Li, FF

Abstract

Soundtracks of multimedia files are information
rich sources, from which much content-related information and
metadata can be extracted. There exist many individual
algorithms for the recognition and analysis of speech, music or
event sounds, allowing for information embedded in audio
format files to be retrieved or represented in a semantic fashion.
However, soundtracks are typically a mixture these three different types of signals, and sometimes overlapped. Segmentation and classification therefore become essential
pre-processors for audio based information retrieval and
metadata generation. This paper stresses the importance of a
universal audio indexing and segmentation pre-processor,
proposes a high-level architecture for such a system, and
presents signal processing algorithms based on soft-computing and two important but neglected feature spaces to improve the accuracy of classification.

Citation

Li, F. (2014). Soft-computing audio classification as a pre-processor for automated content descriptor generation. International journal of computer and communication engineering (Online), 3(2), 101-104. https://doi.org/10.7763/IJCCE.2014.V3.300

• Journal Article Type: Article
• Online Publication Date: Mar 1, 2014
• Publication Date: Mar 1, 2014
• Deposit Date: May 9, 2016
• Journal: International Journal of Computer and Communication Engineering
• Volume: 3
• Issue: 2
• Pages: 101-104
• DOI: https://doi.org/10.7763/IJCCE.2014.V3.300
• Publisher URL: http://dx.doi.org/10.7763/IJCCE.2014.V3.300
• Related Public URLs: http://www.ijcce.org/