SmartMusicKIOSK: music listening station with chorus-search function
Proceedings of the 16th annual ACM symposium on User interface software and technology
Estimation of musical sound separation algorithm effectiveness employing neural networks
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
A comparative evaluation of search techniques for query-by-humming using the MUSART testbed
Journal of the American Society for Information Science and Technology
Automatic transcription of melody, bass line, and chords in polyphonic music
Computer Music Journal
Source/filter model for unsupervised main melody extraction from polyphonic audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Score-informed audio decomposition and applications
Proceedings of the 21st ACM international conference on Multimedia
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This paper describes a robust method for estimating the fundamental frequency (F0) of melody and bass lines in monaural real-world musical audio signals containing sounds of various instruments. Most previous F0-estimation methods had great difficulty dealing with such complex audio signals because they were designed to deal with mixtures of only a few sounds. To make it possible to estimate the F0 of the melody and bass lines, we propose a predominant-F0 estimation method called PreFEst that does not rely on the F0's unreliable frequency component and obtains the most predominant F0 supported by harmonics within an intentionally limited frequency range. It evaluates the relative dominance of every possible F0 by using the expectation-maximization algorithm and considers the temporal continuity of F0s by using a multiple-agent architecture. Experimental results show that our real-time system can detect the melody and bass lines in audio signals sampled from commercially distributed compact discs.