Information Retrieval
Comparison of different implementations of MFCC
Journal of Computer Science and Technology
Signal Processing Methods for Music Transcription
Signal Processing Methods for Music Transcription
Psychoacoustics: Facts and Models
Psychoacoustics: Facts and Models
Initialization enhancer for non-negative matrix factorization
Engineering Applications of Artificial Intelligence
A Generalized Divergence Measure for Nonnegative Matrix Factorization
Neural Computation
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
Monaural music source separation: nonnegativity, sparseness, and shift-invariance
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
IEEE Transactions on Audio, Speech, and Language Processing
Musical source separation using time-frequency source priors
IEEE Transactions on Audio, Speech, and Language Processing
Separation of synchronous pitched notes by spectral filtering of harmonics
IEEE Transactions on Audio, Speech, and Language Processing
Automatic Piano Transcription Using Frequency and Time-Domain Information
IEEE Transactions on Audio, Speech, and Language Processing
A connectionist approach to automatic transcription of polyphonic piano music
IEEE Transactions on Multimedia
Unsupervised analysis of polyphonic music by sparse coding
IEEE Transactions on Neural Networks
A general modular framework for audio source separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
IEEE Transactions on Neural Networks
Algorithms for nonnegative matrix factorization with the β-divergence
Neural Computation
Multiple instrument mixtures source separation evaluation using instrument-dependent NMF models
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Multiple fundamental frequency estimation based on sparse representations in a structured dictionary
Digital Signal Processing
Journal of Intelligent Information Systems
Automatic music transcription: challenges and future directions
Journal of Intelligent Information Systems
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Multiple pitch estimation consists of estimating the fundamental frequencies and saliences of pitched sounds over short time frames of an audio signal. This task forms the basis of several applications in the particular context of musical audio. One approach is to decompose the short-term magnitude spectrum of the signal into a sum of basis spectra representing individual pitches scaled by time-varying amplitudes, using algorithms such as nonnegative matrix factorization (NMF). Prior training of the basis spectra is often infeasible due to the wide range of possible musical instruments. Appropriate spectra must then be adaptively estimated from the data, which may result in limited performance due to overfitting issues. In this paper, we model each basis spectrum as a weighted sum of narrowband spectra representing a few adjacent harmonic partials, thus enforcing harmonicity and spectral smoothness while adapting the spectral envelope to each instrument. We derive a NMF-like algorithm to estimate the model parameters and evaluate it on a database of piano recordings, considering several choices for the narrowband spectra. The proposed algorithm performs similarly to supervised NMF using pre-trained piano spectra but improves pitch estimation performance by 6% to 10% compared to alternative unsupervised NMF algorithms.