Ashish R. Panat

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An electroencephalogram (EEG) is a procedure that records brain wave patterns, which are used to identify abnormalities related to the electrical activities of the brain. In this study an effective algorithm is proposed to automatically classify EEG clips into two different classes: normal and abnormal. For categorizing the EEG data, feature extraction(More)
Pitch and formant frequencies are important features in speech which are used to identify the emotional state of a person. The Pitch and Formants are first extracted from the speech signal and then their analysis is carried out to recognize 3 different emotional states of the person. The emotions considered are Neutral, Happy and Sad. The TU-Berlin database(More)
This paper describes the performance of k-NN classifier to classify the different emotions. The human brain is a superimposition of the diverse processes. This complex structure of brain is recognized through EEG signals. EEG signals indicate the changes in the state of brain. Electroencephalograph (EEG) measurements are commonly used in different research(More)
Fast Fourier Transform is an essential data processing technique in communication systems and DSP systems. In this brief, we propose high speed and area efficient 64 point FFT processor using Vedic algorithm. To reduce computational complexity and area, we develop FFT architecture by devising a radix-4 algorithm and optimizing the realization by Vedic(More)
The Human Machine Interface (HMI) is the technology that enables direct communication between the human brain and the other external devices. Emotion recognition, thus, plays an important role in the design of HMI. Electroencephalogram (EEG) shows the internal emotional state changes of a person very effectively as compared to other traditional methods such(More)
This paper presents a novel emotion transformation scheme of speech signal which is text independent and speaker independent. Speech signals as many other signals are inherently multi-scale in nature, owing to contributions from events occurring with different localizations in time and frequency. Therefore, emotion dependent spectral parameters those(More)
In this paper we propose the support vector machine classifier for the purpose of classifying Human Emotions using Electroencephalogram (EEG). EEG signal consists of different brain waves reflecting brain activity according to the electrode placement and the functioning of the brain. Audio Visual stimuli are given to the human volunteers and emotions in the(More)
Speech emotion recognition is one of the interested research topics in today's time. Presently there are many attempts have been made for emotions recognition. The speech features such as energy and formants are extracted from speech. Angry, stress, admiration, teasing and shocking, these emotional states have been recognized on the basis of speech feature(More)
—It is known that one of the essential building blocks of turbo codes is the interleaver and its design using random, semi-random (S-Random) and deterministic permutations. In this paper, two new types of turbo code interleavers, Enhanced Block S-Random (EBSR) interleaver is proposed. The design algorithm for the new interleaver is described in depth, and(More)