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Hybridisation of Mel Frequency Cepstral Coefficient and Higher Order Spectral Features for Musical Instruments Classification

Bhalke D. G., Rama Rao C. B., Bormane D.

Archives of Acoustics

2016

Vol. 41, No. 3

427--436

This paper presents the classification of musical instruments using Mel Frequency Cepstral Coefficients (MFCC) and Higher Order Spectral features. MFCC, cepstral, temporal, spectral, and timbral features have been widely used in the task of musical instrument classification. As music sound signal is generated using non-linear dynamics, non-linearity and non-Gaussianity of the musical instruments are important features which have not been considered in the past. In this paper, hybridisation of MFCC and Higher Order Spectral (HOS) based features have been used in the task of musical instrument classification. HOS-based features have been used to provide instrument specific information such as non-Gaussianity and non-linearity of the musical instruments. The extracted features have been presented to Counter Propagation Neural Network (CPNN) to identify the instruments and their family. For experimentation, isolated sounds of 19 musical instruments have been used from McGill University Master Sample (MUMS) sound database. The proposed features show the significant improvement in the classification accuracy of the system.

Amplitude-Frequency Monitoring of Power Quality Transients using Higher-Order Statistics and Self-Organizing Neural Networks

Gonzalez de la Rosa J. J., Aguera-Perez A., Palomares-Salas J. C., Moreno-Munoz A.

Przegląd Elektrotechniczny

2011

R. 87, nr 8

128-137

In this paper a smart automatic classification of PQ transients is performed attending to their amplitudes and frequencies, and the extreme of higher-order cumulants. Feature extraction stage is double folded. First, these statistical measurements reveal the hidden geometry for a constant amplitude or frequency, conforming the 2D clustering grace to the third and fourth-order features associated to each signal anomaly, coupled to the 50-Hz power line. Precisely the main contribution of the work is the novel finding that the maxima and the minima of the higher-order cumulants distribute according to curves families, each of which associated to the transient's frequency or amplitude. Given a statistical order, each datum in a curve corresponds to the initial amplitude (or constant frequency), and to a couple of extremes (min-max) associated to the statistical estimator. The random grouping along each curve reveals the a priori hidden geometry, linked to the subjacent electrical phenomenon. Secondly, the regular surface grid in the input space (amplitude-frequency) experiments a transformation to the output space which is developed by the higher-order statistics. Once the geometry in the feature space has been found, we show the computational intelligence modulus, based in Self-Organizing Maps, which performs satisfactory learning along each frequency and amplitude curve. Performance of a four-neuron network with different geometries is shown, confirming the curves' patterns.

W artykule opisano automatyczną metodę klasyfikacji jakości energii w stanach przejściowych z uwzględnieniem amplitudy, częstotliwości i wartości ekstremalnych. W pierwszym etapie przeprowadzane są pomiary statystyczne dla stałej amplitudy i częstotliwości uwzględniające klastry 2D i właściwości trzeciego i czwartego rzędu towarzyszące anomaliom. Następnie uwzględniana jest geometria sieci. Po tym etapie włączany jest moduł sztucznej inteligencji bazujący na sieciach neuronowych.