Abstract
A typical content-based audio management system deals with three aspects namely audio segmentation and classification, audio analysis, and content-based retrieval of audio. In this paper, we integrate the three aspects of content-based audio management into a single framework and propose an efficient method for flexible querying and browsing of auditory data. More specifically, we utilize two robust feature sets namely MPEG-7 Audio Spectrum Flatness (ASF) and Mel Frequency Cepstral Coefficients (MFCC) as the underlying features in order to improve the content-based retrieval accuracy, since both features have some advantages for distinct types of audio (e.g., music and speech). The proposed system provides a wide range of opportunities to query and browse an audio data by content, such as querying and browsing for a chorus section, sound effects, and query-by-example. In addition, the clients can express their queries in the form of point, range, and k-nearest neighbor, which are particularly significant in the multimedia domain.
Keywords
- Similarity Matrix
- Range Query
- Point Query
- Audio Data
- Audio Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Sert, M., Baykal, B., Yazıcı, A. (2006). Structural and Semantic Modeling of Audio for Content-Based Querying and Browsing. In: Larsen, H.L., Pasi, G., Ortiz-Arroyo, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2006. Lecture Notes in Computer Science(), vol 4027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766254_27
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DOI: https://doi.org/10.1007/11766254_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34638-8
Online ISBN: 978-3-540-34639-5
eBook Packages: Computer ScienceComputer Science (R0)