Text independent speaker recognition system using Back Propagation Network with wavelet features

Albin, A. Jose; Nandhitha, N. M.; Roslin, S. Emalda

doi:10.1109/iccsp.2014.6949910

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…In this paper, feasibility of soft computing technique for determining the message bits from one-bit corrupted codewords is studied. A five layered Back Propagation Network is used for obtaining the message bits from the corrupted codewords [6,7,8]. Instead of choosing random numbers for testing, the proposed technique is tested with image intensity values and performance is measured in terms of Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) for the edge detected image.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of less complex Viterbi decoder based on neural networks for effective transmission of medical images

Balamurugan¹,

Nandhitha²

2015

ces

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Convolution coding is the most commonly used channel encoding technique for reliable data transmission. At the receiver's end, Viterbi decoder is used for extracting the message bits. Though these decoders can correct any number of errors, complexity of the decoder increases with the number of bit errors. Hence research began in this area, to design decoders which are less complex and yet provides accurate results. In this paper, a five layered feed forward neural network is developed to decode the image pixels which are convolutionaly encoded. The decoded intensities are then converted into an image and the edges are detected from the images. Edges are regarded as useful information because segmentation of the Region of Interest (ROI) is performed on the edge detected image. Performance of the proposed technique is measured in terms of Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR). It is found that irrespective of the images, RMSE is significantly less and PSNR is high and the true edges are retained in the image. The proposed technique is scalable as it is not dependent on the size of the image but on the gray levels of the image.

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Section: Introductionmentioning

confidence: 99%

Feasibility of less complex Viterbi decoder based on neural networks for effective transmission of medical images

Balamurugan¹,

Nandhitha²

2015

ces

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LSTM Based Speaker Recognition System with Optimized Features

Nandhitha

Roslin

Rajasekhar

2023

2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)

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Speaker Identification based on Hybrid Feature Extraction Techniques

AbuAladas¹,

Akram²,

Shaban³

et al. 2019

ijacsa

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One of the most exciting areas of signal processing is speech processing; speech contains many features or characteristics that can discriminate the identity of the person. The human voice is considered one of the important biometric characteristics that can be used for person identification. This work is concerned with studying the effect of appropriate extracted features from various levels of discrete wavelet transformation (DWT) and the concatenation of two techniques (discrete wavelet and curvelet transform) and study the effect of reducing the number of features by using principal component analysis (PCA) on speaker identification. Backpropagation (BP) neural network was also introduced as a classifier.

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Text independent speaker recognition system using Back Propagation Network with wavelet features

Cited by 4 publications

References 3 publications

Feasibility of less complex Viterbi decoder based on neural networks for effective transmission of medical images

Feasibility of less complex Viterbi decoder based on neural networks for effective transmission of medical images

LSTM Based Speaker Recognition System with Optimized Features

Speaker Identification based on Hybrid Feature Extraction Techniques

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