Music Classification Based on Genre and Mood
| Autor: | Mehang Rai, Basanta Joshi, Ayush Shakya, Mahendra Singh Thapa, Bijay Gurung |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g. HCI) business.industry Computer science Speech recognition ComputingMilieux_PERSONALCOMPUTING 020206 networking & telecommunications 02 engineering and technology Digital media Arousal Support vector machine 030507 speech-language pathology & audiology 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION Mood 0202 electrical engineering electronic engineering information engineering Music information retrieval The Internet Valence (psychology) 0305 other medical science business ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Communications in Computer and Information Science ISBN: 9789811064296 CICBA (2) |
| DOI: | 10.1007/978-981-10-6430-2_14 |
| Popis: | The advent of internet and the growing number of digital media has increased the necessity of Music Information Retrieval systems within which Music Classification is a prominent task. Here, we present methods to perform genre based classification over five different genre and mood based classification using a mood model that maps mood onto a two-dimensional space along axes of arousal and valence. Support vector machine and a feed-forward artificial neural network are applied to achieve an overall accuracy of 88% for genre based classification and 73% and 67% accuracy for the arousal and valence axes respectively in mood based classification. |
| Databáze: | OpenAIRE |
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