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Evolving a multi-classifier system with cartesian genetic programming for multi-pitch estimation of polyphonic piano music
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| This paper presents a new method for multi-pitch estimation on piano recordings. We propose a framework based on a set of classifiers to analyze the audio input and identify the piano notes present on the given audio signal. Our system's classifiers were evolved using Cartesian Genetic Programming: we take advantage of Cartesian Genetic Programming to evolve a set of mathematical functions that act as independent classifiers for piano notes. Our latest improvements are also presented, including test results using F-measure metrics. Our system architecture is also described to show the feasibility of its parallelization and implementation as a real time system. The proposed approach achieved competitive results, when compared to the state of the art. | 1.57 MB | Adobe PDF |
Orientador(es)
Resumo(s)
This paper presents a new method for multi-pitch estimation on piano recordings. We propose a framework based on a set of classifiers to analyze the audio input and identify the piano notes present on the given audio signal. Our system's classifiers were evolved using Cartesian Genetic Programming: we take advantage of Cartesian Genetic Programming to evolve a set of mathematical functions that act as independent classifiers for piano notes. Our latest improvements are also presented, including test results using F-measure metrics. Our system architecture is also described to show the feasibility of its parallelization and implementation as a real time system. The proposed approach achieved competitive results, when compared to the state of the art.
Descrição
Conference date - March 22 - 26, 2021
Palavras-chave
Genetic Programming Multi pitch Estimation Automatic piano music transcription piano notes detection
Contexto Educativo
Citação
Rolando Miragaia, Francisco Fernandez de Vega, and Gustavo Reis. 2021. Evolving a multi-classifier system with cartesian genetic programming for multi-pitch estimation of polyphonic piano music. In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC '21). Association for Computing Machinery, New York, NY, USA, 472–480. https://doi.org/10.1145/3412841.3441927.
Editora
Association for Computing Machinery (ACM)
Licença CC
Sem licença CC