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Este artículo describe el conjunto de experimentos realizados para obtener el reconocimiento de 60 notas musicales de un piano digital por medio de técnicas de procesamiento digital de señales y clasificadores. Para la etapa de técnicas de procesamiento digital de señales se utilizaron: Frecuencia fundamental, coeficientes Cepstrales en la Frecuencia de Mel y Cepstrales de Mecánica Coclear. Para los clasificadores se consideraron Modelos Ocultos de Markov usando HTK (Hidden Markov Model Toolkit), Modelos de Mixturas Gaussianas, Redes Neuronales Artificiales y Cuantificación Vectorial. El corpus creado para este trabajo consistió de 720 notas comprendidas en un rango de cinco octavas. Se usaron 12 archivos para cada una de las 60 notas a reconocer, las cuales se grabaron a una velocidad 70 bpm (beats per minute). Los mejores resultados se obtuvieron al aplicar coeficientes CMCC como técnica y Modelos Ocultos de Markov como clasificador (100% de reconocimiento en cada octava). Igualmente se obtuvo el 100% de reconocimiento por octava al usarse coeficientes MFCC como técnica y Cuantificación Vectorial como clasificador. Palabras clave: Transcripción automática de música, procesamiento digital de señales, reconocimiento de patrones. Comparison of Classifiers for Recognition of Musical Notes Abstract. This paper describes the set of experiments performed for recognition of 60 musical notes of a digital piano using digital signal processing techniques and classifiers. In the stage of digital signal processing techniques we used: Fundamental frequency (Pitch), Mel-Frequency Cepstral Coefficients (MFCC) and Cochlear Mechanics Cepstral Coefficients (CMCC). The implemented classifiers were: Hidden Markov Models (HMM) using HTK (Hidden Markov Model Toolkit), Gaussian Mixture Models (GMM), Neuronal Networks and Vector Quantization (VQ). The created corpus for this work consists of 720 notes included in a range of five octaves. We used 12 files for each one of the 60 notes for recognition, which were recorded in a speed of 70 beats per minute. The best results were obtained applying CMCC as technic and HMM as classifier (100% This paper describes the set of experiments performed for recognition of 60 musical notes of a digital piano using digital signal processing techniques and classifiers. In the stage of digital signal processing techniques we used: Fundamental frequency (Pitch), Mel-Frequency Cepstral Coefficients (MFCC) and Cochlear Mechanics Cepstral Coefficients (CMCC). The implemented classifiers were: Hidden Markov Models (HMM) using HTK (Hidden Markov Model Toolkit), Gaussian Mixture Models (GMM), Neuronal Networks and Vector Quantization (VQ). The created corpus for this work consists of 720 notes included in a range of five octaves. We used 12 files for each one of the 60 notes for recognition, which were recorded in a speed of 70 beats per minute. The best results were obtained applying CMCC as technic and HMM as classifier (100%

Comparación de clasificadores para el reconocimiento de notas musicales