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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gopi, E.S. Viswanathan, V. Sankaralingham, P. Ramakumar, S. |
| Copyright Year | 2005 |
| Description | Author affiliation: Electron. & Commun. Eng. department, Sri Venkateswara Coll. of Eng., Chennai, India (Gopi, E.S.; Viswanathan, V.; Sankaralingham, P.; Ramakumar, S.) |
| Abstract | Existing search engines utilize text representing the required audio clips as the index for searching and retrieving the same from the huge database. Instead, a six dimensional high level feature vector based on the human feelings experienced on listening to the audio clips can be used as the index for searching, which is comparatively better in obtaining the optimal audio clips since it takes into account the user's subjective perception of similarity. The high level feature vector is defined as the vector consisting of the ranks assigned by humans based on the emotions experienced while listening to a song, such as happy, sad etc. However, it is not practical to assign a human ranked high level feature vector for the millions of songs available in the database. Hence, in this paper, we propose a technique of mapping the low level features of a song such as cepstrum and harmonicity to its high level features. |
| Sponsorship | Minist. of Educ. (MOE) of PR China Hong Kong Univ. of Sci. & Technol. (HKUST) Univ. of Electron. Sci. and Technol. of China (UESTC) City Univ. of Hong Kong |
| File Size | 590360 |
| File Format | |
| ISBN | 0780390156 |
| DOI | 10.1109/ICCCAS.2005.1495280 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-05-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Humans Cepstrum Artificial neural networks Search engines Data engineering Educational institutions Spatial databases Indexes Testing |
| Content Type | Text |
| Resource Type | Article |
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