The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.
The paper analyzes the estimation of the fundamental frequency from the real speech signal which is obtained by recording the speaker in the real acoustic environment modeled by the MP3 method. The estimation was performed by the Picking-Peaks algorithm with implemented parametric cubic convolution (PCC) interpolation. The efficiency of PCC was tested for Catmull-Rom, Greville, and Greville two- parametric kernel. Depending on MSE, a window that gives optimal results was chosen.
The authors developed a simple and efficient method, called the Coupled Displacement method, to study the linear free vibration behavior of the moderately thick rectangular plates in which a single-term trigonometric/algebraic admissible displacement, such as total rotations, are assumed for one of the variables (in both X,Y directions), and the other displacement field, such as transverse displacement, is derived by making use of the coupling equations. The coupled displacement method makes the energy formulation to contain half the number of unknown independent coefficients in the case of a moderately thick plate, contrary to the conventional Rayleigh-Ritz method. The smaller number of undetermined coefficients significantly simplifies the vibration problem. The closed form expression in the form of fundamental frequency parameter is derived for all edges of simply supported moderately thick rectangular plate resting on Pasternak foundation. The results obtained by the present coupled displacement method are compared with existing open literature values wherever possible for various plate boundary conditions such as all edges simply supported, clamped and two opposite edges simply supported and clamped and the agreement found is good.
Estimating the fundamental frequency and harmonic parameters is basic for signal modelling in a power supply system. Differing from the existing parameter estimation algorithms either in power quality monitoring or in harmonic compensation, the proposed algorithm enables a simultaneous estimation of the fundamental frequency, the amplitudes and phases of harmonic waves. A pure sinusoid is obtained from an input multiharmonic input signal by finite-impulse-response (FIR) comb filters. Proposed algorithm is based on the use of partial derivatives of the processed signal and the weighted estimation procedure to estimate the fundamental frequency, the amplitude and the phase of a multi-sinusoidal signal. The proposed algorithm can be applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The simulation results verify the effectiveness of the proposed algorithm.
In Western music culture instruments have been developed according to unique instrument acoustical features based on types of excitation, resonance, and radiation. These include the woodwind, brass, bowed and plucked string, and percussion families of instruments. On the other hand, instrument performance depends on musical training, and music listening depends on perception of instrument output. Since musical signals are easier to understand in the frequency domain than the time domain, much effort has been made to perform spectral analysis and extract salient parameters, such as spectral centroids, in order to create simplified synthesis models for musical instrument sound synthesis. Moreover, perceptual tests have been made to determine the relative importance of various parameters, such as spectral centroid variation, spectral incoherence, and spectral irregularity. It turns out that the importance of particular parameters depends on both their strengths within musical sounds as well as the robustness of their effect on perception. Methods that the author and his colleagues have used to explore timbre perception are: 1) discrimination of parameter reduction or elimination; 2) dissimilarity judgments together with multidimensional scaling; 3) informal listening to sound morphing examples. This paper discusses ramifications of this work for sound synthesis and timbre transposition.