Multi Pitch Estimation of Piano Music using Cartesian Genetic Programming with Spectral Harmonic Mask

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Miragaia:2020:SSCI,

• author = "Rolando Miragaia and Gustavo Reis and Francisco Fernandez {de Vega} and Francisco Chavez",
• title = "Multi Pitch Estimation of Piano Music using Cartesian Genetic Programming with Spectral Harmonic Mask",
• booktitle = "2020 IEEE Symposium Series on Computational Intelligence (SSCI)",
• year = "2020",
• pages = "1800--1807",
• month = dec,
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
• DOI = "doi:10.1109/SSCI47803.2020.9308178",
• abstract = "Piano notes recognition, or pitch estimation of piano notes has been a popular research topic for many years, and is still investigated nowadays. It is a fundamental task during the process of automatic music transcription (extracting the musical score from an acoustic signal). We take advantage of Cartesian Genetic Programming (CGP) to evolve mathematical functions that act as independent classifiers for piano notes. These classifiers are then used to identify the presence of piano notes in polyphonic audio signals. This paper describes our technique and the latest improvements made in our research. The main feature is the introduction of spectral harmonic masks in the binarization process for measuring the fitness values that has allowed to improve the classification rate: 1percent in the F-measure mean result. Our system architecture is also described to show the feasibility of its parallelization, which will reduce the computing time.",
• notes = "Also known as \cite{9308178}",

}

Genetic Programming entries for Rolando Miragaia Gustavo Miguel Jorge dos Reis Francisco Fernandez de Vega Francisco Chavez de la O

Citations