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Emotion Semantic Similarity Based Music Information Retrieval Model

Posted on:2012-11-26Degree:MasterType:Thesis
Country:ChinaCandidate:L J ZhouFull Text:PDF
GTID:2178330335954505Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
As the rapid development of science and technology, the fast increase of digital storage capacity makes it possible that enormous multimedia data can be stored and managed automatically. The exploration of online shared music provides more choices to users as well more challenges to music sharing web service systems. The problem of understanding the real intent of system users and recommending them with most relevant music items has been increasingly challenging. In Music Information Retrieval (MIR) System, many system users submit queries that contain no descriptive information about music. This kind of query with no desciptive information are defined as "Non-Descriptive Query" in this paper, and usually can not be tackled well in common music search and download websites. It is urgent to find a solution to understand the implicit emotional inquiry of systems users and compute the similarity between music and non-descriptive queries.Affective Computing is a significant research area in Human Computer Interaction. The main purpose is to recognize the emotion of computer users and serve them emotionally. As music is sentimentally expressive, the problem of processing Non-Descriptive Queries can be addressed via detecting implicit emotion. Our study on new model of Music Information Retrieval (MIR) is based on text emotion detection and recognition techniques. Queries and music are represented in a high dimensional emotion space and the similarity is computed according to their relevance in the high dimensional emotion space.The focus of this paper is on modeling music in emotion space, including:First, we define music emotion space according to WordNet-Affect. The categories are extended to 7 types considering the intrinsic feature of music.Second, we download a large dataset from last.fm and build DUTMIR-Dataset with manual annotation, which applied in our machine learning theories based music emotion classification and produce 3 types of different feature sets to evaluate their influence on MIR.Third, as music data is short, concise and implicit, we utilize LDA model to attach recommended tags to music, which conquers the sparsity and imbalance of the music dataset. Besides, different classifiers are tested to get the best one for MIR system.Last but not least, in order to show our model in an obvious way, we design and develop a prototype system.
Keywords/Search Tags:Music Retrieval, Music Emotion Similarity, Social Annotation, Machine Learning
PDF Full Text Request
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