Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: FrFT

Sortuj według:

Ogranicz wyniki do:

Asymmetric cryptosystem with random decomposition using fractional Fourier and Fourier transforms

Gaur Kapil Shankar, Singh Hukum, Thakran Seema

Optica Applicata

2023

Vol. 53, nr 4

591--602

In this paper, an asymmetric cryptosystem based on random decomposition is proposed. The suggested scheme used three different decryption keys to get decrypted image, two of which are generated using phase truncation and one through random decomposition. The combination of these keys and fractional Fourier transform parameter increase the security of cryptosystem against various attacks. MATLAB simulations are used to validate the scheme’s conclusions. The effectiveness of a scheme is validated by the key sensitivity performance of the cryptosystem. This research also includes a 3D plot for both grayscale and binary images. Correlation coefficient (CC) values between the original and recovered images is also calculated to validate the cryptosystem.

Automatic Genre Classification Using Fractional Fourier Transform Based Mel Frequency Cepstral Coefficient and Timbral Features

Bhalke D. G., Rajesh B., Bormane D. S.

Archives of Acoustics

2017

Vol. 42, No. 2

213--222

This paper presents the Automatic Genre Classification of Indian Tamil Music and Western Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted from music excerpts has been analysed, to identify the appropriate feature descriptors for the two major genres of Indian Tamil music, namely Classical music (Carnatic based devotional hymn compositions) & Folk music and for western genres of Rock and Classical music from the GTZAN dataset. The results for Tamil music have shown that the feature combination of Spectral Roll off, Spectral Flux, Spectral Skewness and Spectral Kurtosis, combined with Fractional MFCC features, outperforms all other feature combinations, to yield a higher classification accuracy of 96.05%, as compared to the accuracy of 84.21% with conventional MFCC. It has also been observed that the FrFT based MFCC effieciently classifies the two western genres of Rock and Classical music from the GTZAN dataset with a higher classification accuracy of 96.25% as compared to the classification accuracy of 80% with MFCC.