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Nearest-Neighbor Automatic Sound Classification with a WordNet Taxonomy
| Title | Nearest-Neighbor Automatic Sound Classification with a WordNet Taxonomy |
| Publication Type | Journal Article |
| Year of Publication | 2005 |
| Authors | Cano, P. , Koppenberger M. , Le Groux S. , Ricard J. , Wack N. , & Herrera P. |
| Journal Title | Journal of Intelligent Information Systems |
| Volume | 24 |
| Pages | 99-111 |
| Abstract | Sound engineers need to access vast collections of sound efects for their film and video productions. Sound efects providers rely on text-retrieval techniques to offer their collections. Currently, annotation of audio content is done manually, which is an arduous task. Automatic annotation methods, normally fine-tuned to reduced domains such as musical instruments or reduced sound effects taxonomies, are not mature enough for labeling with great detail any possible sound. A general sound recognition tool would require first, a taxonomy that represents the world and, second, thousands of classifiers, each specialized in distinguishing little details. We report experimental results on a general sound annotator. To tackle the taxonomy definition problem we use WordNet, a semantic network that organizes real world knowledge. In order to overcome the need of a huge number of classifiers to distinguish many different sound classes, we use a nearest-neighbor classifier with a database of isolated sounds unambiguously linked to WordNet concepts. A 30% concept prediction is achieved on a database of over 50.000 sounds and over 1600 concepts. |
| preprint/postprint document | http://hdl.handle.net/10230/41883 |
