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This paper presents a multimodal approach for person verification based on the features extracted from signature, face and iris of an individual. Features from signatures are extracted using Discrete Cosine Transform (DCT) and by applying Sparse Representation techniques. Facial features are extracted using Gabor Filter bank and Kernel Principal Component Analysis (KPCA). In this work, for extracting...
Automatic speaker recognition system identifies a person from the information contained in the speech signal. These systems are the most user-friendly means of biometric recognition and are being used in applications like teleconferencing, banking, forensics etc. The accuracy of these depends on the methods used to extract features from the speech signal, modeling methods, classifiers used to identify...
Voice biometric provides a person’s unique identity and hence is widely employed for security applications. Here, Text Independent Speaker Recognition system is implemented using Gaussian Mixture Models (GMM) and i-Vector method with two features PNCC (Power Normalized Cepstral Coefficients) and RASTA PLP (Relative Spectral Perceptual Linear Prediction) coefficients. It was observed that accuracy...
The performance of speech classification tasks can be improved by accurate acoustic modeling. This modelling is responsible for establishing the relationship between the speech signal and the phonetic units that were produced by the speaker. In this paper Acoustic Modeling(AM) is done using Reservoir Computing(RC) technique for which the input speech signal frames can be identified and classified...
The state of art automatic speech recognition systems use Deep Neural Networks(DNN) for acoustic modeling. More recently, Convolutional neural Networks(CNN) have shown substantial acoustic modelling capabilities due to its ability to deal with structural locality in the feature space. In this paper, a detailed study of CNN based acoustic models on TIMIT database has been performed. For feature extraction...
The two major applications of speaker recognition applications are speaker verification and speaker identification. But in most of the cases the signal is corrupted with background interferences such as noise and echo. This paper proposes the method of speaker recognition and identification after the noise separation. Support Vector Machine(SVM) classification based signal separation is adopted here...
Steganography is one of the newly emerging area of development in the field of signal processing. It is the technique of hiding the fact that communication is even taking place, by hiding information in any other information. In this paper, a secure data-hiding method is proposed for hiding multiple datatypes such as text, image and audio within images by Secure Adaptive Pixel Pair Matching(SAPPM)...
This paper presents a novel and efficient audio signal recognition algorithm with limited computational complexity. As the audio recognition system will be used in real world environment where background noises are high, conventional speech recognition techniques are not directly applicable, since they have a poor performance in these environments. So here, we introduce a new audio recognition algorithm...
Consultative Committee for Space Data Systems (CCSDS) data compression consists of a two dimensional discrete wavelet transform of the image, followed by bit-plane coding of the transformed data. The algorithm can provide both lossless and lossy compression, and allows a user to directly control the compressed data volume or the fidelity with which the wavelet-transformed data can be reconstructed...
This paper presents the application of two different image enhancement techniques to medical images and the comparison of these techniques with traditional Histogram Equalization (HE) method. The proposed method uses Weighted Histogram Equalization (WHE) and transform domain approach to enhance medical images. Simulation results shows that Perona-Malik filter (PM filter) can be used to remove the...
This paper presents the hardware implementation of a frequency converter and AC/DC step up converter for realising the low voltage energy harvesting. In the simulation part, the input of the AC/DC converter is from an electromagnetic micro generator which converts vibration into electrical energy. The electromagnetic micro generator is being modelled and simulated. But in the hardware implementation,...
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