Please use this identifier to cite or link to this item:
Title: Music Content Analysis on Audio Quality and Its Application to Music Retrieval
Keywords: live music, audio quality assessment, learning-to-rank, music information retrieval, i2MUSE
Issue Date: 13-Jan-2015
Source: CAI JINGLI (2015-01-13). Music Content Analysis on Audio Quality and Its Application to Music Retrieval. ScholarBank@NUS Repository.
Abstract: Nowadays, more and more users are uploading their music recordings of live music concerts to video sharing websites such as YouTube. The audio quality of these uploads, however, varies widely due to their recording conditions, and most existing video search engines do not take the audio quality into consideration when ranking their search results. We proposed a learning-to-rank technique for ranking live music recordings on YouTube according to audio quality and constructed three datasets. Several ?song-level? audio feature representations and learning-to-rank algorithms were studied. Specifically, we explore various segmentation methods and fusion system to account for the temporal characteristics of live music. A comprehensive performance study is conducted to demonstrate the effectiveness of the proposed system through both objective and subjective evaluations. Moreover, we apply the result into our practical system i2MUSE to help the users to search and find good music to do exercise.
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Revised_CAI_JINGLI(A0095623)_Thesis.pdf2.06 MBAdobe PDF



Page view(s)

checked on Jan 19, 2018


checked on Jan 19, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.