Singing Transcription from Polyphonic Music Using Melody Contour Filtering

Automatic singing transcription and analysis from polyphonic music records are essential in a number of indexing techniques for computational auditory scenes. To obtain a note-level sequence in this work, we divide the singing transcription task into two subtasks: melody extraction and note transcri...

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Bibliographic Details
Published in:Applied sciences 2021-07, Vol.11 (13), p.5913
Main Authors: He, Zhuang, Feng, Yin
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
audio melody extraction
audio signal analysis
Audio signals
Computer applications
Contours
Fourier transforms
Image processing
Information processing
Information retrieval
Instrumental music
Melody
melody contour
Mirex
Music
music information retrieval
Musical performances
Neural networks
Polyphony
Rhythms
Salience
Signal processing
Singing
singing transcription
Transcription
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Summary:Automatic singing transcription and analysis from polyphonic music records are essential in a number of indexing techniques for computational auditory scenes. To obtain a note-level sequence in this work, we divide the singing transcription task into two subtasks: melody extraction and note transcription. We construct a salience function in terms of harmonic and rhythmic similarity and a measurement of spectral balance. Central to our proposed method is the measurement of melody contours, which are calculated using edge searching based on their continuity properties. We calculate the mean contour salience by separating melody analysis from the adjacent breakpoint connective strength matrix, and we select the final melody contour to determine MIDI notes. This unique method, combining audio signals with image edge analysis, provides a more interpretable analysis platform for continuous singing signals. Experimental analysis using Music Information Retrieval Evaluation Exchange (MIREX) datasets shows that our technique achieves promising results both for audio melody extraction and polyphonic singing transcription.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11135913