The produced sound quality of guitar primarily depends on vibrational characteristics of the resonance box. Also, the tonal quality is influenced by the correct combination of tempo along with pitch, harmony, and melo...The produced sound quality of guitar primarily depends on vibrational characteristics of the resonance box. Also, the tonal quality is influenced by the correct combination of tempo along with pitch, harmony, and melody in order to find music pleasurable. In this study, the resonance frequencies of the modelled resonance box have been analysed. The free-free modal analysis was performed in <em>ABAQUS</em> to obtain the modes shapes of the un-constrained sound box. To find music pleasing to the ear, the right pitch must be set, which is achieved by tuning the guitar strings. In order to analyse the sound elements, the Fourier analysis method was chosen and implemented in <em>MATLAB</em>. Identification of fundamentals and overtones of the individual string sounds were carried out prior and after tuning the string. The untuned strings showed irregular fundamental frequencies and higher partials of decay. Octaves and power spectrums have been presented and discussed in this paper.展开更多
The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focus...The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.展开更多
文摘The produced sound quality of guitar primarily depends on vibrational characteristics of the resonance box. Also, the tonal quality is influenced by the correct combination of tempo along with pitch, harmony, and melody in order to find music pleasurable. In this study, the resonance frequencies of the modelled resonance box have been analysed. The free-free modal analysis was performed in <em>ABAQUS</em> to obtain the modes shapes of the un-constrained sound box. To find music pleasing to the ear, the right pitch must be set, which is achieved by tuning the guitar strings. In order to analyse the sound elements, the Fourier analysis method was chosen and implemented in <em>MATLAB</em>. Identification of fundamentals and overtones of the individual string sounds were carried out prior and after tuning the string. The untuned strings showed irregular fundamental frequencies and higher partials of decay. Octaves and power spectrums have been presented and discussed in this paper.
文摘The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.
