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.

In this paper, the two-temperature thermoelasticity model is proposed to a specific problem of a thermoelastic semi-infinite solid. The bounding plane surface of the semi-infinite solid is considered to be under a non-Gaussian laser pulse. Generalized thermoelasticity analysis with dual-phase-lags is taken into account to solve the present problem. Laplace transform and its inversion techniques are applied and an analytical solution as well as its numerical outputs of the field variables are obtained. The coupled theory and other generalized theory with one relaxation time may be derived as special cases. Comparison examples have been made to show the effect of dual-phase-lags, temperature discrepancy, laser-pulse and laser intensity parameters on all felids. An additional comparison is also made with the theory of thermoelasticity at a single temperature.