Hemanta Kumar Palo ¹, Jyoti Mohanty ¹, Mihir Narayan Mohanty ¹, Mahesh Chandra ²

Affiliations
1 Department of Electronics and Communication Engineering, Siksha ‘O’ Anusandhan University, Bhubaneswar - 751030, Odisha, IN
2 Department of Electronics and Communication Engineering, Birla Institute Technology, Ranchi - 835215, Jharkhand, IN

Abstract

Background/Objectives: The objective of this paper is to distinguish similar and overlapped emotional states like disgust and irritation from the primary angry state based on similarity measures. Methods/Statistical Analysis: The similarity test among these emotions have been carried out using the signal waveform, frequency coherence, power spectral density, log-likelihood score and Dynamic Time Wrapping (DTW) technique. Findings: Disgust state has more number of frequency coherence peaks which are leaning more towards unity like angry state. Further the phase angle between irritation and anger state is larger than between anger and disgust state. The minimum cost path using dynamic time wrapping technique founds to be 147.5917 between disgust and angry state as compared to 184.2386 between angry and irritation samples. From these analyses, it is concluded that, the primary anger state is more closure to disgust state than irritation state. The results are promising and we are able to put a boundary among these emotional states with our chosen techniques. Application/Improvements: Anger detection can lead to an improvement in human relationship and social system refinement. Demarcation of subcategory emotions that leads to anger state prevents confusion and can improve its recogntion.

Keywords

Emotional States, Dynamic Type Wrapping, Short Time Fourier Transform, Similarity Measure, Spectral Coherence.

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Shipra ¹, Mahesh Chandra ²

Affiliations
1 Electronics and Communication Engineering Department, BIT Mesra, Near Patna Airport, Patna - 800014, Bihar, IN
2 Electronics and Communication Engineering Department, BIT Mesra, Ranchi - 835215, Jharkhand, IN

Abstract

This paper present a novel hybridized QCN-PNCC features. These features are obtained by processing Power Normalized Cepstral Coefficients (PNCC) with Quantile based Dynamic Cepstral Normalization Technique (QCN). The robustness of the QCN-PNCC features is compared with PNCC features for the task of Hindi Vowel classification with HMM classifier for Context-Dependent and Context- Independent cases in clean as well as in noisy environment. It is observed that the recognition accuracy of QCN-PNCC features with Hidden Markov Model (HMM) as classifier exhibit an improvement of approximately 8% as compared to PNCC features for Hindi vowel classification task.

Keywords

Power normalized Cepstral Coefficient (PNCC), QCN, QCN-PNCC, Speech Recognition.

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Garima Gupta ¹, V. K. Gupta ¹, Mahesh Chandra ²

Affiliations
1 Department of Electronics and Communication Engineering, Inderprastha Engineering College, Ghaziabad – 201010, Uttar Pradesh, IN
2 Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, BIT, Mesra, Ranchi - 835215, Jharkhand, IN

Abstract

As now a day’s data transmission is used widely. Data can be audio, video or text. A large data is present on the net today. Video data is mostly used now as it is much easier to understand. So, websites like you tube etc. are gaining more and more popularity. As a huge data is present on the net so there must be a sound security system for this data also. Owner of the data is putting his data on the net so that he may get revenue and popularity from it. But due to easy copy pasting hacking of data is also common. Any hacker can make a copy of data from his name and can transfer it ahead. Watermarking seems a copyright protection tool in this case. In this paper various methods of video watermarking are implemented in mat lab and a comparison in between different methods is made. Three methods are taken here – spatial domain, DWT domain, DCT domain. These methods are compared according to some common parameters.

Keywords

DCT, DWT, MSE, PSNR, Spatial Domain

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