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• W2891814570 abstract "Spectral features are limited in modeling musical signals with multiple concurrent pitches due to the challenge to suppress the interference of the harmonic peaks from one pitch to another. In this paper, we show that using multiple features represented in both the frequency and time domains with deep learning modeling can reduce such interference. These features are derived systematically from conventional pitch detection functions that relate to one another through the discrete Fourier transform and a nonlinear scaling function. Neural networks modeled with these features outperform state-of-the-art methods while using less training data." @default.
• W2891814570 created "2018-09-27" @default.
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• W2891814570 date "2018-04-01" @default.
• W2891814570 modified "2023-10-10" @default.
• W2891814570 title "Automatic Music Transcription Leveraging Generalized Cepstral Features and Deep Learning" @default.
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• W2891814570 doi "https://doi.org/10.1109/icassp.2018.8462079" @default.
• W2891814570 hasPublicationYear "2018" @default.
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