Multiple fundamental frequency estimation based on harmonicity and spectral smoothness

By Anssi Klapuri. IEEE Transactions on Speech and Audio Processing, 2003

366 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e48c650c64c398a1674542bdbfbb42c773e7e2d2

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In Signal Processing

Citations in the corpus

Automatic music transcription: challenges and future directions (2013, background): “pitch salience function (also called pitch strength function) or a pitch candidate set score function (Klapuri 2003; Pertusa and Iñesta 2008; Yeh 2008).”
An End-to-End Neural Network for Polyphonic Piano Music Transcription (2015, background): “being transcribed [2], [3], while the issues relating to a large output space are dealt with by constraining the models to have a maximum polyphony [4], [5].”
Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle (2010, neutral): “Conversely the multipitch estimation (MPE) problem has become a rather active research area in the last decade [8]–[11] and is mostly handled by processing spectral or time–frequency representations.”
Automatic genre classification of music content: a survey (2006, background): “For the estimation of multiple fundamental frequencies of concurrent musical sounds, one may refer to [10] while chord extraction is addressed e.”
Multi-Pitch Estimation (2009, background): “riodic waveforms, is a difficult one, and one that has received much less attention than the single-pitch case, though notable exceptions can be found in [15,1,16,17].”
Joint DOA and multi-pitch estimation based on subspace techniques (2012, methodology): “, various modification of the auto-correlation function [1,7], maximum likelihood, optimal filtering, and subspace techniques [2,3,8].”
Analysis of the meter of acoustic musical signals (2006, background): “The particular motivation for the present work is to utilize metrical information in further signal analysis and in music transcription [6]–[8].”
A Survey of Audio-Based Music Classification and Annotation (2011, methodology): “For frame level pitch analysis, various multi-pitch estimation algorithms have been developed [94], [95] to identify the top candidate pitches for each”