Feature Extraction of Musical Instrument Tones using FFT and Segment Averaging

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). Secondly, the left half of the transformed signal was divided into a number of segments. Finally, the averaging results of that segments, was the feature extraction of the input signal. Based on the test results, the proposed feature extraction was highly efficient for the tones, which have many significant local peaks in the Fourier transform domain, because it only required at least four feature extraction coefficients, in order to represent every tone

The influence of sampling frequency on tone recognition of musical instruments

Sampling frequency of musical instruments tone recognition generally follows the Shannon sampling theorem. This paper explores the influence of sampling frequency that does not follow the Shannon sampling theorem, in the tone recognition system using segment averaging for feature extraction and template matching for classification. The musical instruments we used were bellyra, flute, and pianica, where each of them represented a musical instrument that had one, a few, and many significant local peaks in the Discrete Fourier Transform (DFT) domain. Based on our experiments, until the sampling frequency is as low as 312 Hz, recognition rate performance of bellyra and flute tones were influenced a little since it reduced in the range of 5%. However, recognition rate performance of pianica tones was not influenced by that sampling frequency. Therefore, if that kind of reduced recognition rate could be accepted, the sampling frequency as low as 312 Hz could be used for tone recognition of musical instruments

Segmentasi Kata Tulisan Tangan Menggunakan Jendela Blackman

Dalam pengenalan kata tulisan tangan, salah satu strateginya adalah mengenali huruf demi huruf yang menyusun kata tersebut. Dengan strategi ini, bila kata yang akan dikenali ditulis dengan ragam latin, pengenalan huruf demi hurufnya menjadi persoalan yang rumit, karena tidak jelasnya segmentasi antara huruf yang satu dengan huruf yang lain. Untuk mengatasi persoalan tersebut, dapat digunakan suatu metode segmentasi yang dinamakan segmentasi lebih. Tulisan ini membahas metode segmentasi lebih menggunakan jendela Blackman. Secara ringkas, proses segmentasi dalam tulisan ini sebagai berikut: Masukan – Pengolahan awal – Segmentasi – Keluaran. Masukan berupa sebuah citra kata terisolasi dalam format biner, serta keluaran berupa sejumlah citra segmen huruf. Pengolahan awal berfungsi untuk mengkoreksi slope dan slant dari citra masukan. Koreksi-koreksi ini diperlukan karena metode segmentasi yang digunakan sensitif terhadap slope dan slant. Segmentasi berfungsi untuk memecah citra kata menjadi sejumlah citra segmen huruf. Berdasarkan hasil pengujian secara subyektif terlihat bahwa, untuk semua jendela Blackman yang lebarnya 8, 12, dan 16 titik, dengan nilai alpha masing-masing mulai dari 0 ; 0,12 ; dan 0,40 dapat digunakan secara efektif untuk keperluan segmentasi. Secara umum, jendela Blackman dengan kelebaran mulai dari 8 titik, bila kelebaran dan nilai alpha-nya makin naik, dapat digunakan secara efektif untuk keperluan segmentasi

Water Pump Mechanical Faults Display at Various Frequency Resolutions

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

The Recognition Of Semaphore Letter Code Using Haar Wavelet And Euclidean Function

Semaphore are one way of communicating over long distances using the semaphore flags. In Indonesia semaphore is used in scout activities as a method to send information in the form of a sentence containing the message. Sending the semaphore letter code tends to be difficult. Based on the need to semaphore learning, this research proposes an algorithm with image processing as a way to correct the movement of the semaphore letter code based on the image obtained by using the webcam. Digital image processing, Wavelet feature extraction, and Euclidean distance function are applied in this study to determine the best recognition rate of variation decimation and distance variation to sending semaphore letter code using the webcam. This study resulted in the best recognition rate of 95.4% in the 1 st decimation, recognition rate reached 94.6% in decimation 2, and recognition rate reached 94.2% in decimation 3. The result of the introduction of the semaphore letter code is on the introduction of movement as far as 3 to 5 meter