Visualizing music and audio using self-similarity
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Speech Detection Based on Hilbert-Huang Transform
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
Speech pitch determination based on Hilbert-Huang transform
Signal Processing
Audio genre classification using percussive pattern clustering combined with timbral features
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Misual: music visualization based on acoustic data
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Extraction and visualization of the repetitive structure of music in acoustic data: misual project
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
Music Homogeneity Analysis through Instantaneous Frequencies
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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This paper describes a new method of music genre recognition. Even for people it is difficult to define musical genres, because a genre is something more than a set of rules. Automation of this task could improve the work of multiple audio-related WEB portals, such as audio-libraries, and could simplify human activity in other music-related areas. For music genre recognition, we introduce the instantaneous frequency spectrum (IFS) whose calculation is based on the Hilbert-Huang transform. In our method, IFSs of audio signals are generated from their instantaneous frequencies and used to calculate music genre templates. The experimental results for three test music pieces show that the method can accurately detect and differentiate genres of tunes. Slicing test music into frames and recognizing genres for short fragments of a whole music piece gives a precise description of a piece's internal structure, which could help to enhance people's understanding of the music. Presentation of this information also is an advance in music visualization.