Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Extracting information from music audio
Communications of the ACM - Music information retrieval
A discriminative model for polyphonic piano transcription
EURASIP Journal on Applied Signal Processing
Automatic transcription of melody, bass line, and chords in polyphonic music
Computer Music Journal
Pattern induction and matching in music signals
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
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The melody of a musical piece--informally, the part you would hum along with--is a useful and compact summary of a full audio recording. The extraction of melodic content has practical applications ranging from content-based audio retrieval to the analysis of musical structure. Whereas previous systems generate transcriptions based on a model of the harmonic (or periodic) structure of musical pitches, we present a classification-based system for performing automatic melody transcription that makes no assumptions beyond what is learned from its training data. We evaluate the success of our algorithm by predicting the melody of the ADC 2004 Melody Competition evaluation set, and we show that a simple frame-level note classifier, temporally smoothed by post processing with a hidden Markov model, produces results comparable to state of the art model-based transcription systems.