Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/16228
Title: Content-based music classification, summarization and retrieval
Authors: SHAO XI
Keywords: music information retrieval, music classification, music summarization,
Issue Date: 5-Apr-2007
Source: SHAO XI (2007-04-05). Content-based music classification, summarization and retrieval. ScholarBank@NUS Repository.
Abstract: With the explosive amount of music data available on the internet in recent years, there has been a compelling need for the end user to search and retrieve effectively in increasingly large digital music collection. In order to manage the real-world digital music database, some applications are needed to help people manipulate the large digital music database. In this work, three issues in real world digital music database management were tackled. These issues include music summarization, music genre classification and music retrieval by human humming, as these three applications satisfy the basic requirement of an operational real world music database management system. Among these three applications, music genre classification and music summarization perform music analysis and find the structure information both for the individual songs in database and the whole music database, which can speed up the searching process, while music retrieval is an interactive application. In this thesis, these issues were addressed using machine learning approaches, complementary to digital signal processing method. To be specific, the digital signal processing helps extract compact, task dependent information-bearing representation from raw acoustic signals, i.e., music summarization and classification employ timber features and rhythm features to characterize the music content, while music retrieval by humming requires the melody features to characterize the music content. Machine learning includes segmentation, classification, clustering and similarity measuring, etc., and it pertains to computer understanding of the music contents. We proposed an adaptive clustering approach for structuring the music content in music summarization, extended the current music genre classification by a supervised hierarchical classification approach and an unsupervised classification approach, and in query by humming, in order to separate the vocal content from the polyphonic music, we proposed a statistical learning based method to solve the permutation inconsistency problem for Frequency-Domain Independent Component Analysis. In most cases, the proposed algorithms for these three applications have been evaluated by conducting user studies, and the experimental results indicated the proposed algorithms were effective in helping realize usersa?? expectations in manipulating the music database. In general, since the semantic gap exists between low level representation of music signals and different level applications in music database management, machine learning is indispensable to bridge such gap. Furthermore, machine learning approach can also be incorporated into signal processing to solve difficult problems.
URI: http://scholarbank.nus.edu.sg/handle/10635/16228
Appears in Collections:Ph.D Theses (Open)

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