Bi-Modal Deep Boltzmann Machine Based Musical Emotion Classification
| Autor: | Tom Arjannikov, Nan Jiang, Zhang Xiong, Wenge Rong, Moyuan Huang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Emotion classification Boltzmann machine 02 engineering and technology Musical computer.software_genre Lyrics Task (project management) 030507 speech-language pathology & audiology 03 medical and health sciences Mood 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Active listening Artificial intelligence 0305 other medical science business computer Natural language processing |
| Zdroj: | Artificial Neural Networks and Machine Learning – ICANN 2016 ISBN: 9783319447803 ICANN (2) |
| DOI: | 10.1007/978-3-319-44781-0_24 |
| Popis: | Music plays an important role in many people’s lives. When listening to music, we usually choose those music pieces that best suit our current moods. However attractive, automating this task remains a challenge. To this end the approaches in the literature exploit different kinds of information (audio, visual, social, etc.) about individual music pieces. In this work, we study the task of classifying music into different mood categories by integrating information from two domains: audio and semantic. We combine information extracted directly from audio with information about the corresponding tracks’ lyrics using a bi-modal Deep Boltzmann Machine architecture and show the effectiveness of this approach through empirical experiments using the largest music dataset publicly available for research and benchmark purposes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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