Ekstrasi Ciri dan Pengenalan Suara Vokal Bahasa Indonesia Berdasarkan Jenis Kelamin secara Real TIME

Suara manusia memiliki ciri yang beraneka ragam, sehingga dapat dijadikan media komunikasi yang efektif. Oleh karena itu banyak penelitian yang berkaitan dengan suara manusia dilakukan untuk meningkatkan pengenalan suara. Proses pengembangan pengenalan suara dilakukan secara realtime berdasarkan jenis kelamin untuk menghasilkan akurasi yang tepat dalam batas waktu yang telah ditentukan. Metode Discrete Wavelet Transform (DWT) level 3 dan Dynamic Time Wraping (DTW) digunakan sebagai metode ekstrasi ciri dan metode pengenalan suara. Pada metode ekstrasi ciri Discrete Wavelet Transform (DWT) level 3 didapatkan 8 buah ciri. Sedangkan metode pengenalan suara menggunakan Dynamic Time Wraping (DTW) dilakukan dengan menghitung diskriminasi jarak terkecil antara dua ciri yang berbeda tanpa dilakukan pelatihan terlebih dahulu. Pengenalan suara diujikan pada 6 orang penutur pria dan 6 orang penutur wanita secara bergantian dengan masing-masing data pengukuran 900 pasang. Hasil persentase rata-rata pengenalan akurasi terbaik mencapai 54,6% dari pengujian terhadap 6 orang penutur pria secara bergantian dan 54,17 % dari pengujian terhadap 6 orang penutur wanita secara bergantian dari masing-masing pasangan data yang diperoleh secara realtime

Fault diagnosis of induction motor using wavelets

Wavelet analysis is one the prime signal processing and data analysis techniques its ability to locate fault at exact duration is one of its added advantage in comparison to Fast Fourier transform in which time information of signal is lost .In addition to this FFT has another disadvantage that at low loads sidebands converges so it becomes difficult to detect the faults. STFT was proposed as a solution to resolution problem but its limited time-frequency resolution capability, because the uncertainty principle. Low frequencies can be scarcely depicted with short window that’s why wavelet theory is yet the most powerful tool in data analysis of signals. This paper presents a study of fault detection of induction motor like broken rotor bars stator short circuits using wavelets envelope analysis in which a signal is converted into approximate and detail signals thus removing the fundamental current which hides the fault harmonics present during faults

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

The Affine Uncertainty Principle, Associated Frames and Applications in Signal Processing

Uncertainty relations play a prominent role in signal processing, stating that a signal can not be simultaneously concentrated in the two related domains of the corresponding phase space. In particular, a new uncertainty principle for the affine group, which is directly related to the wavelet transform has lead to a new minimizing waveform. In this thesis, a frame construction is proposed which leads to approximately tight frames based on this minimizing waveform. Frame properties such as the diagonality of the frame operator as well as lower and upper frame bounds are analyzed. Additionally, three applications of such frame constructions are introduced: inpainting of missing audio data, detection of neuronal spikes in extracellular recorded data and peak detection in MALDI imaging data

Block-level discrete cosine transform coefficients for autonomic face recognition

This dissertation presents a novel method of autonomic face recognition based on the recently proposed biologically plausible network of networks (NoN) model of information processing. The NoN model is based on locally parallel and globally coordinated transformations. In the NoN architecture, the neurons or computational units form distributed networks, which themselves link to form larger networks. In the general case, an n-level hierarchy of nested distributed networks is constructed. This models the structures in the cerebral cortex described by Mountcastle and the architecture based on that proposed for information processing by Sutton. In the implementation proposed in the dissertation, the image is processed by a nested family of locally operating networks along with a hierarchically superior network that classifies the information from each of the local networks. The implementation of this approach helps obtain sensitivity to the contrast sensitivity function (CSF) in the middle of the spectrum, as is true for the human vision system. The input images are divided into blocks to define the local regions of processing. The two-dimensional Discrete Cosine Transform (DCT), a spatial frequency transform, is used to transform the data into the frequency domain. Thereafter, statistical operators that calculate various functions of spatial frequency in the block are used to produce a block-level DCT coefficient. The image is now transformed into a variable length vector that is trained with respect to the data set. The classification was done by the use of a backpropagation neural network. The proposed method yields excellent results on a benchmark database. The results of the experiments yielded a maximum of 98.5% recognition accuracy and an average of 97.4% recognition accuracy. An advanced version of the method where the local processing is done on offset blocks has also been developed. This has validated the NoN approach and further research using local processing as well as more advanced global operators is likely to yield even better results

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research