Multifeature speech/music discrimination based on mid-term level statistics and supervised classifiers
| Autor: | Maha Charfeddine, Chokri Ben Amar, Henri Nicolas, Eya Mezghani |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Speech recognition Feature extraction Statistical parameter 020207 software engineering Pattern recognition 02 engineering and technology Standard deviation Svm classifier ComputingMethodologies_PATTERNRECOGNITION Cepstrum Statistics 0202 electrical engineering electronic engineering information engineering Entropy (information theory) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | AICCSA |
| Popis: | Speech and music discrimination task is considered among the most important tools in several multimedia applications. In this paper, we propose a twofold approach for speech/music discrimination: in the first side, we consider a relevant and rich set of features then in the second one, we adopt mid-term level statistics. The used set of features involves musical descriptors as well as speech and cepstral descriptors. According to the retrieved results, standard deviation metric was elected as the best mid-term level statistic parameter. The SVM classifier has provided the higher accuracy value among the set of employed classifiers. And when combined to the standard deviation statistical parameter, it has reached a satisfying accuracy percentage higher than 99%. Thus, the proposed scheme has achieved promising classification performance thanks to the discriminating abilities and diversity of the used features besides to the statistics on mid term level. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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