Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News
| Autor: | Theodoros Theodorou, Nikos Fakotakis, Iosif Mporas, Alexandros Lazaridis |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Audio mining
business.industry Computer science Feature vector Speech recognition Pattern recognition Feature selection 02 engineering and technology Data-driven Support vector machine 030507 speech-language pathology & audiology 03 medical and health sciences Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 0305 other medical science Cluster analysis business Sound recognition Radio broadcasting |
| Zdroj: | International Journal on Artificial Intelligence Tools. 26:1750005 |
| ISSN: | 1793-6349 0218-2130 |
| DOI: | 10.1142/s0218213017500051 |
| Popis: | Aiming to an automatic sound recognizer for radio broadcasting events, a methodology of clustering the audio feature space using the discrimination ability of the audio descriptors as a criterion, is investigated in this work. From a given and close set of audio events, commonly found in broadcast news transmissions, a large set of audio descriptors is extracted and their data-driven ranking of relevance is clustered, providing a more robust feature selection. The clusters of the feature space are feeding machine learning algorithms implemented as classification models during the experimental evaluation. This methodology showed that support vector machines provide significantly good results, considering the achieved accuracy due to their ability of coping well in high dimensionality experimental conditions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|