A feature extraction for musical instrument tones that based on a transform domain approach was proposed in this paper. The aim of the proposed feature extraction was to get the lower feature extraction coefficients. In general, the proposed feature extraction was carried out as follow. Firstly, the input signal was transformed using FFT (Fast Fourier Transform)
This paper proposes a feature extraction method for a chord recognition, which gives a fewer number of feature extraction coefficients than the previous works. The method of the proposed feature extraction is segment averaging with SHPS (Simplified Harmonic Product Spectrum) and logarithmic scaling. The chords used in developing the proposed feature extraction were guitar chords. In a more detail, the method of the proposed feature extraction basically is as follows. Firstly, the input signal is transformed using FFT (Fast Fourier Transform). Secondly, the left portion of the transformed signal is then processed in succession using SHPS, logarithmic scaling, and segment averaging. The output of segment averaging is the result of the proposed feature extraction. Based on the test results, the proposed feature extraction is quite efficient for use in chord recognition, since it requires only at least eight coefficients to represent each chord.
When an electrical machine suffered a mechanical fault, it generally emits certain sounds. These sounds came from the vibration. Therefore, based on the vibration, it could be detected if there was a mechanical fault in an electrical machine. This paper discussed the graphical display of the vibration of electrical machines in the form of household water pumps which were in good condition, faulty bearing, faulty impeller, or faulty foot valve. Vibration could be displayed in the time domain, or in the frequency domain, by using the three axes, i.e. X, Y, and Z. In the frequency domain, the vibration could be displayed at various frequency resolutions. Based on the observations, the higher frequency resolution, the lower detail in the graphical display of frequency domain would be shown. Although there was lower detail in the graphical display of frequency domain, at frequency resolution of 11.7 Hz in the X axis, showed that it could be visually distinguished among water pumps which were in good condition, faulty bearing, faulty impeller, or faulty foot valve.
In the field of handwritten word recognition field, word segmentation into letters is an approach that could be used. Using this approach, word segmentation would be a complicated task, especially when dealing with a cursive handwritten word. A simple method in word segmentation called oversegmentation could be used. This paper discusses a simple method using Kaiser window.In general, the word segmentation process in this paper can be described as follow: Input -Preprocessing -SegmentationOutput. The input is an image of isolated handwritten word in binary format, while the output is images of letter segment. The main purpose of preprocessing is to correct slant and slope. This preprocessing is necessary since the segmentation method used is sensitive with slant and slope. The main purpose of segmentation is to divide a word into some letter segments.Based on a subjective test result, it was shown that the minimum parameters for the Kaiser window that can be used effectively for oversegmentation are 8 points in window's length and 10 in beta value. As its window's length is getting longer and its beta value is getting bigger, it can also be used effectively for oversegmentation. However, it must be noted that if the letter size is getting bigger, there will be more letter segments resulted.
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