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- Multi Pitch Estimation of Piano Music using Cartesian Genetic Programming with Spectral Harmonic MaskPublication . Miragaia, Rolando; Reis, Gustavo; Fernandéz de Vega, Francisco; Chávez, FranciscoPiano 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: 10% 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.
- Cooperative and decomposable approaches on royal road functionsPublication . Reis, Gustavo; Fernandéz, Francisco; Olague, Gustavo
- Cartesian genetic programming applied to pitch estimation of piano notesPublication . Inacio, Tiago; Miragaia, Rolando; Reis, Gustavo; Grilo, Carlos; Fernandez, FranciscoPitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. This paper presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP). We take advantage of evolutionary algorithms, in particular CGP, to evolve mathematical functions that act as classifiers. These classifiers are used to identify piano notes' pitches in an audio signal. For a first approach, the obtained results are very promising: our error rate outperforms two of three state-of-the-art pitch estimators.