985 resultados para Piano trios
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The extended program notes include historical background on the composers and pieces being performed, as well as the analytical form regarding the works: Variations on "Ah vous dirai-je-maman," K. 265 by Wolfgang Amadeus Mozart; Sonata in C minor, op. 13 "Pathétique," by Ludwig van Beethoven; Consolation No. 3 and Funerailles by Franz Liszt; Fantasie in F minor, op. 49 by Frédéric Chopin.
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Mi labor como pianista y docente en dos países, tanto en España como en Argentina, me ha motivado para la realización de esta tesis. He sentido preocupación al observar los nervios e inseguridades sufridos por los alumnos cuando llega el momento de los conciertos, exámenes o concursos; y debido a ello, he notado que la calidad de la ejecución y la experiencia de la situación del concierto, examen o concurso se ven perjudicadas. Este trabajo no pretende ser un estudio psicológico ni una herramienta terapéutica, sino simplemente un aporte pedagógico, tanto para el alumno en su futuro como músico profesional como para la labor didáctica de los profesores de piano de grado superior. El nerviosismo es un tema clave recurrente en las conversaciones entre compañeros antes de salir al escenario e incluso es también un tema de justificación para los ejecutantes de todo lo que hubiera podido suceder, tanto positiva como negativamente, si no los hubiese sufrido en el transcurso de la interpretación. Es muy poca o casi nula la atención que pedagógicamente se dedica a los estudiantes de música sobre la experiencia escénica en el aprendizaje instrumental. Además, es curioso constatar cómo los centros de enseñanzas musicales preparan a los alumnos para ser instrumentistas pero no les brindan el 100% de las herramientas que necesitan para serlo. Se otorga una preparación basada en la técnica del instrumento, la estética, la comprensión analítica, armónica y morfológica de las obras, la historia de las distintas épocas y compositores, entre otras, todo ello a través de asignaturas obligatorias. Pero estas materias raramente son aplicadas a la hora de hacer una interpretación instrumental. Los alumnos, incluso algunos profesores de instrumento, no establecen una interrelación entre las diferentes asignaturas.Todo eso llevaría a una preparación musical bastante completa si no nos detenemos a observar que falta una preparación mental. Esta carencia se hace presente en el momento de salir a escena o en la presentación ante un tribunal al observar errores, bloqueos, dificultad de concentración y la posterior autocrítica...
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Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. The goal of Pitch Estimation is to find the pitch or fundamental frequency of a digital recording of a speech or musical notes. It plays an important role, because it is the key to identify which notes are being played and at what time. Pitch Estimation of real instruments is a very hard task to address. Each instrument has its own physical characteristics, which reflects in different spectral characteristics. Furthermore, the recording conditions can vary from studio to studio and background noises must be considered. This dissertation 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 explore and evolve complex mathematical functions that act as classifiers. These classifiers are used to identify piano notes pitches in an audio signal. To help us with the codification of the problem, we built a highly flexible CGP Toolbox, generic enough to encode different kind of programs. The encoded evolutionary algorithm is the one known as 1 + , and we can choose the value for . The toolbox is very simple to use. Settings such as the mutation probability, number of runs and generations are configurable. The cartesian representation of CGP can take multiple forms and it is able to encode function parameters. It is prepared to handle with different type of fitness functions: minimization of f(x) and maximization of f(x) and has a useful system of callbacks. We trained 61 classifiers corresponding to 61 piano notes. A training set of audio signals was used for each of the classifiers: half were signals with the same pitch as the classifier (true positive signals) and the other half were signals with different pitches (true negative signals). F-measure was used for the fitness function. Signals with the same pitch of the classifier that were correctly identified by the classifier, count as a true positives. Signals with the same pitch of the classifier that were not correctly identified by the classifier, count as a false negatives. Signals with different pitch of the classifier that were not identified by the classifier, count as a true negatives. Signals with different pitch of the classifier that were identified by the classifier, count as a false positives. Our first approach was to evolve classifiers for identifying artifical signals, created by mathematical functions: sine, sawtooth and square waves. Our function set is basically composed by filtering operations on vectors and by arithmetic operations with constants and vectors. All the classifiers correctly identified true positive signals and did not identify true negative signals. We then moved to real audio recordings. For testing the classifiers, we picked different audio signals from the ones used during the training phase. For a first approach, the obtained results were very promising, but could be improved. We have made slight changes to our approach and the number of false positives reduced 33%, compared to the first approach. We then applied the evolved classifiers to polyphonic audio signals, and the results indicate that our approach is a good starting point for addressing the problem of Pitch Estimation.
Music of Today Series presents The 21st- Century Piano with Cristina Valdes, piano February 26, 2013
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