A Similar Music Retrieval Scheme Based on Musical Mood Variation

Music evokes various human emotions or creates music moods through low level musical features. In fact, typical music consists of one or more moods and this can be used as an important factor for determining the similarity between music. In this paper, we propose a new music retrieval scheme based o...

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Bibliographic Details
Main Authors: Sanghoon Jun, Byeong-jun Han, Eenjun Hwang
Format: Conference Proceeding
Language:English
Subjects:
Acoustic signal detection
Clustering algorithms
Computer vision
Content based retrieval
Database systems
Deductive databases
feature extraction
Humans
k-means clustering algorithm
longest common subsequence
Mood
Multiple signal classification
Music information retrieval
music mood
Similar music retrieval
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Summary:Music evokes various human emotions or creates music moods through low level musical features. In fact, typical music consists of one or more moods and this can be used as an important factor for determining the similarity between music. In this paper, we propose a new music retrieval scheme based on the mood change pattern. For this, we first divide music clips into segments based on low level musical features. Then, we apply K-means clustering algorithm for grouping them into clusters with similar features. By assigning a unique mood symbol for each group, each music clip can be represented into a sequence of mood symbols. Then, we estimate the similarity of music based on the similarity of their musical mood sequence using the longest common subsequence (LCS) algorithm. To evaluate the performance of our scheme, we carried out various experiments and measured the user evaluation. We report some of the results.
DOI:10.1109/ACIIDS.2009.65