Browsing by Author "Thirukumaran, S."

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Analysis of Emotional Speech Recognition Using Artificial Neural Network
(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2016) Archana, A.F.C.; Thirukumaran, S.
This paper presents an artificial neural network based approach for analyzing the classification of emotional human speech. Speech rate and energy are the most basic features of speech signal but they still have significant differences between emotions such as angry and sad. The feature pitch is frequently used in this work and auto-correlation method is used to detect the pitch in each of the frames. The speech samples used for the simulations are taken from the dataset Emotional Prosody Speech and Transcripts in the Linguistic Data Consortium (LDC). The LDC database has a set of acted emotional speeches voiced by the males and females. The speech samples of only four emotions categories in the LDC database containing both male and female emotional speeches are used for the simulation. In the speech pre-processing phase, the samples of four basic types of emotional speeches sad, angry, happy, and neutral are used. Important features related to different emotion states are extracted to recognize speech emotions from the voice signal then those features are fed into the input end of a classifier and obtain different emotions at the output end. Analog speech signal samples are converted to digital signal to perform the pre-processing. Normalized speech signals are segmented in frames so that the speech signal can maintain its characteristics in short duration. 23 short term audio signal features of 40 samples are selected and extracted from the speech signals to analyze the human emotions. Statistical values such as mean and variance have been derived from the features. These derived data along with their related emotion target are fed to train using artificial neural network and test to make up the classifier. Neural network pattern recognition algorithm has been used to train and test the data and to perform the classification. The confusion matrix is generated to analyze the performance results. The accuracy of the neural network based approach to recognize the emotions improves by applying multiple times of training. The overall correctly classified results for two times trained network is 73.8%, whereas it is 83.8% when increasing the training times to ten. The overall system provides a reliable performance and correctly classifying more than 80% emotions after properly trained.
Artificial Neural Network based Emotions Recognition System for Tamil Speech.
(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2017) Paranthaman, D.; Thirukumaran, S.
Emotion has become the important part in communication between human and machine, so the detection of emotions has become important part in pattern recognition through Artificial Neural Network (ANN). Human's emotions can be detected based on the physiological measurements, facial expressions and speech. Since human shows different expressions for a particular emotion when they are speaking therefore the emotions can be quantified. The English speech dataset is provided with descriptions of each emotional context available in Emotional Prosody Speech and Transcripts in the Linguistic Data Consortium (LDC). The main objective of this project describes the ANN based approach for Tamil speech emotions recognition by analyzing four basic emotions sad, angry, happy and neutral using the mid-term features. Tamil speeches are recorded with four emotions by males and females using the software “Cubase”. The time duration is set to three seconds with the sampling frequency of 44.1 kHz as it is the logical and default choice for most digital audio material. For the simulations, these recorded speech samples are categorized into different datasets and 40 samples are included in each dataset. Preprocessing includes sampling, normalization and segmentation and is performed on the speech signals. In the sampling process the analog signals are converted into digital signals then each speech sentence is normalized to ensure that all the sentences are in the same volume range. Next, the signals are separated into frames in the segmentation process. Then, the mid-term features such as speech rate, energy, pitch and Mel Frequency Cepstral Coefficients (MFCC) are extracted from the speech signals. Mean and Variance values are calculated from the extracted features. To create the classifier for the emotions, the above statistical results as an input matrix with their related emotions-target matrix are fed to train, validate and test. The neural network back propagation algorithm is executed by the classifier to recognize completely new samples of Tamil speech datasets. Each of the datasets consists of different combinations of speech sentences with different emotions. Then, the new speech samples are assigned to identify the recognition rate of the speech emotions using the confusion matrix. In conclusion, the selected mid-term features of Tamil speech signals classify the four emotions with the overall accuracy of 83.45%. Thus, the mid-term features selected are proven to be the good representations of emotions for Tamil speech signals and correctly recognize the Tamil speech emotions using ANN. The input gathered by a group of experienced drama artists who have the voice with the good emotional feelings would help to increase the accuracy of the dataset.
Finite Element Method based Triangular Mesh Generation for Aircraft-Lightning Interaction Simulation
(3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Vinotha, K.; Thirukumaran, S.
Lightning is a natural electrical discharge process. Most common lightning strike is Cloud-to-Ground. It occurs when the negative charges accumulated at the bottom of the thundercloud traverse towards the ground to neutralize its charges with the positive earth charges induced due to the thundercloud and electrons travels along the lightning channel. The statistics shows that the commercial aircrafts directly struck by lightning strikes that are under the thundercloud once a year on average. The study of electromagnetic threat due to lightning strikes is important for flight safety and restructuring the aircraft design to mitigate direct lightning effects on the physical material of the aircraft causing damages and indirect effects on the navigation systems in it.The prime objective of this paper is to find the electric field distribution around the aircraft conductor in free space conditions under lightning scenario. For the simulation, the flash of the cloud-to-ground lightning is represented as a wave equation. Finite element method is applied to solve the wave equation for identifying potential distribution and exclusively to electric field calculations. Each of the triangular finite elements are considered and the potential at any nodes within a typical element are obtained. The equation 𝐸 = −𝛻𝑉 represents the relationship between electric potential and electric field which is used to determine the electric field distribution around the aircraft surface by a numerical derivative evaluation technique from the electric potential distribution already obtained. This paper presents an aircraft-lightning interaction simulation under the thundercloud and above the ground by generating two dimensional triangular mesh using finite element method. Significant electric field distribution is observed at the sharp end points of the aircraft. Due to higher radiated electric field, the aircraft-lightning interaction may result in an adverse impact on the aircraft navigation systems and cause damage to its structures. The simulation results would be very useful for studying lightning impact on the aerial vehicles struck by the cloud-to-ground lightning. During the simulation, it was assumed that an aircraft surface is a good conductor and the effects of material properties are left for future studies.
Finite Element Method based Triangular Mesh Generation for Aircraft-Lightning Interaction Simulation
(3rd International Conference on Advances in Computing and Technology (ICACT ‒ 2018), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2018) Vinotha, K.; Thirukumaran, S.
Lightning is a natural electrical discharge process. Most common lightning strike is Cloud-to-Ground. It occurs when the negative charges accumulated at the bottom of the thundercloud traverse towards the ground to neutralize its charges with the positive earth charges induced due to the thundercloud and electrons travels along the lightning channel. The statistics shows that the commercial aircrafts directly struck by lightning strikes that are under the thundercloud once a year on average. The study of electromagnetic threat due to lightning strikes is important for flight safety and restructuring the aircraft design to mitigate direct lightning effects on the physical material of the aircraft causing damages and indirect effects on the navigation systems in it.The prime objective of this paper is to find the electric field distribution around the aircraft conductor in free space conditions under lightning scenario. For the simulation, the flash of the cloud-to-ground lightning is represented as a wave equation. Finite element method is applied to solve the wave equation for identifying potential distribution and exclusively to electric field calculations. Each of the triangular finite elements are considered and the potential at any nodes within a typical element are obtained. The equation represents the relationship between electric potential and electric field which is used to determine the electric field distribution around the aircraft surface by a numerical derivative evaluation technique from the electric potential distribution already obtained. This paper presents an aircraft-lightning interaction simulation under the thundercloud and above the ground by generating two dimensional triangular mesh using finite element method. Significant electric field distribution is observed at the sharp end points of the aircraft. Due to higher radiated electric field, the aircraft-lightning interaction may result in an adverse impact on the aircraft navigation systems and cause damage to its structures. The simulation results would be very useful for studying lightning impact on the aerial vehicles struck by the cloud-to-ground lightning. During the simulation, it was assumed that an aircraft surface is a good conductor and the effects of material properties are left for future studies.