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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67014
Title: | 從音訊到主題:用卷積神經網路學習語意 From Audio to Topics: Learning Semantics with Convolutional Neural Network |
Authors: | Siao-Yun Dai 戴筱芸 |
Advisor: | 鄭卜壬 |
Keyword: | 卷積神經網路,隱含狄利克雷分布,主題模型,音訊, Convolutional Neural Network,LDA,topic model,audio signal, |
Publication Year : | 2017 |
Degree: | 碩士 |
Abstract: | Nowadays, music has become an import part of our lives. As cloud-based streaming service becomes popular, people are more dependent on music. Music as a tool of expressing emotions, it is rich in semantics. In previous genre and mood classification tasks, some people already show that combining lyrics and audio features can improve the results. Their research indicates there are potential relationship between audio and lyrics. Lyrics directly describe a song’s topic, while audio can expand the emotions. Nevertheless, lyrics can be incomplete or missing. If we can learn the topics from audio, we can guess the possible topics for a song without using lyrics. We proposed an unsupervised two-stage method. First, we learn the latent topics in lyrics by topic model. Second, we transfer audio signal to topic distribution via a convolutional neural network. We show that this framework can indeed learns a semantical representation from audio and can be directly applied to song retrievals. We can not only search the songs with lyrics. For those songs without lyrics, i.e. classical songs, we can also provide a reasonable result. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67014 |
DOI: | 10.6342/NTU201702967 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 資訊工程學系 |
Files in This Item:
File | Size | Format | |
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ntu-106-1.pdf Restricted Access | 6.43 MB | Adobe PDF |
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