ICA-FX features for classification of singing voice and instrumental sound

This paper describes a new approach in locating the segments of singing voice in pop musical songs. Initially, GLR distance measure is employed to temporally detect the boundaries of singing voices and instrumental sounds. ICA-FX is then adopted to extract the independent components of acoustic features for SVM classification. Experimental results indicate that ICA-FX can improve the classification performance by significantly reducing the independent com-ponents that are not related to class label information. 1

Automatic lyric alignment in Cantonese popular music.

Wong Chi Hang.Thesis submitted in: October 2005.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 89-94).Abstracts in English and Chinese.Abstract --- p.ii摘要 --- p.iiiAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 2 --- Literature Review --- p.5Chapter 2.1 --- LyricAlly --- p.5Chapter 2.2 --- Singing Voice Detection --- p.6Chapter 2.3 --- Singing Transcription System --- p.7Chapter 3 --- Background and System Overview --- p.9Chapter 3.1 --- Background --- p.9Chapter 3.1.1 --- Audio Mixing Practices of the popular music industry --- p.10Chapter 3.1.2 --- Cantonese lyric writer practice --- p.11Chapter 3.2 --- System Overview --- p.13Chapter 4 --- Vocal Signal Enhancement --- p.15Chapter 4.1 --- Method --- p.15Chapter 4.1.1 --- Non-center Signal Estimation --- p.16Chapter 4.1.2 --- Center Signal Estimation --- p.17Chapter 4.1.3 --- Bass and drum reduction --- p.21Chapter 4.2 --- Experimental Results --- p.21Chapter 4.2.1 --- Experimental Setup --- p.21Chapter 4.2.2 --- Results and Discussion --- p.24Chapter 5 --- Onset Detection --- p.29Chapter 5.1 --- Method --- p.29Chapter 5.1.1 --- Envelope Extraction --- p.30Chapter 5.1.2 --- Relative Difference Function --- p.32Chapter 5.1.3 --- Post-Processing --- p.32Chapter 5.2 --- Experimental Results --- p.34Chapter 5.2.1 --- Experimental Setup --- p.34Chapter 5.2.2 --- Results and Discussion --- p.35Chapter 6 --- Non-vocal Pruning --- p.39Chapter 6.1 --- Method --- p.39Chapter 6.1.1 --- Vocal Feature Selection --- p.39Chapter 6.1.2 --- Feed-forward neural network --- p.44Chapter 6.2 --- Experimental Results --- p.46Chapter 6.2.1 --- Experimental Setup --- p.46Chapter 6.2.2 --- Results and Discussion --- p.48Chapter 7 --- Lyric Feature Extraction --- p.51Chapter 7.1 --- Features --- p.52Chapter 7.1.1 --- Relative Pitch Feature --- p.52Chapter 7.1.2 --- Time Distance Feature --- p.54Chapter 7.2 --- Pitch Extraction --- p.56Chapter 7.2.1 --- f0 Detection Algorithms --- p.56Chapter 7.2.2 --- Post-Processing --- p.64Chapter 7.2.3 --- Experimental Results --- p.64Chapter 8 --- Lyrics Alignment --- p.69Chapter 8.1 --- Dynamic Time Warping --- p.69Chapter 8.2 --- Experimental Results --- p.72Chapter 8.2.1 --- Experimental Setup --- p.72Chapter 8.2.2 --- Results and Discussion --- p.74Chapter 9 --- Conclusion and Future Work --- p.82Chapter 9.1 --- Conclusion --- p.82Chapter 9.2 --- Future Work --- p.83Chapter A --- Publications --- p.85Chapter B --- Symbol Table --- p.86Bibliography --- p.8

Content-based music structure analysis

Ph.DDOCTOR OF PHILOSOPH