Judul: Perbandingan Wavelet Daubechies dan MFCC sebagai ekstraksi ciri pada pengenalan fonem berdasarkan distribusi normal
Abstrak: Speech recognition is speech to text transcription. Speech to text transcription system is a system used to convert a voice signal from a microphone into a single or a set of words. Most research of speech to text transcription used technique which every word in corpus is modeled. It is not effective if we want to develop a large vocabulary speech recognition system which number of words in corpus are more than one thousand words. Therefore, this research developed phoneme recognition with early stage in speech recognition. This research used some stage proces, those are take data, feature extraction, and feature matching. Normal Distribution (Gaussian) is used for feature matching, Wavelet Daubechies and MFCC is used for feature extraction. Corpus on this research consist of 11 words in Indonesian which each word recorded 20 times, 15 times for data training and 5 times for data testing. This research used 13 cepstral coefficients. Phonemes are generated from the segmentation process, and then mhu and sigma be calculated to generate the model. This case produced 26 models. The best accuracy is 90% generated by feature extraction MFCC and 46.92% generated by the Wavelet Daubechies.
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Judul: Perbandingan pemodelan Wavelet dan MFCC sebagai ekstraksi ciri pada pengenalan fonem dengan teknik jaringan syaraf tiruan sebagai classifier
Abstrak: Researches on voice signals have been carried out using various signal processing methods, such as Linear Prediction Coding (LPC), Mel Frequency cepstrum coefficients (MFCC), and Neural Predictive Coding (NPC) which the whole method is based on Fourier transformation. Therefore, comparisons will be made with other approaches based on Wavelet transformation. This research would be the comparison of two feature extraction types, that are Wavelet daubechies and MFCC. Wavelet Transformation has become increasingly popular in signal processing as image and speech. Wavelet transformation has demonstrated good time-frequency localization properties and are appropiate tools for the analysis of non-stationary signals like speech. MFCC feature extraction that computes the cepstral coefficients by considering the human hearing. ANN multilayer perceptron, known as backpropagation used as classifier. From the research that has been done, it can be concluded that the use of Daubechies Wavelet methods as feature extraction on phoneme recognition is not better than MFCC. The accuracy of Daubechies Wavelet method with a 220 hidden neurons ANN learning has achived 36% rate as the best test results. While on phoneme recognition with MFCC method achieved 100% accuracy rate with a 240 hidden neuron learning ANN.
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Judul: Sarcosporidiasis in slaughter animals
Abstrak: Sarcocystis which belong to the generally occuring parasites for they are found almost all over the countries of the world. Infection of Sarcocystis usually throught inges- tion of feces or flesh of infected animals. The pathogenesis of Sarcosporidiasis is considered as benign but when the Sarcocystis die in a host, sarcocystin or sarcosporidin is liberated thereby which lead to condemnation of meat in the abattoir. Besides condemnation of meat, Sarcocystis also causes: loss of appetite, anemia, lameness, weakness in the lion, diarrhea, fever, pyrexia, paralysis, emaciation, neuro logical disturbances, etc. " Amprolium "at the rate of 100 mg/Kg body weight per day has been practiced for prevention or to minimize Sarco- sporidiasis. Treatment must be started during incubation period. Shedding of Sarcocystis sporocysts in the feces of carnivores is the key factor in the spread of Sarcocystis infection. Therefore, control efforts must be based primarily on measures designed to break the cycle of Sarcocystis.
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