DSP Real-Time Implementation of an Audio Compression Algorithm by using the Fast Hartley Transform

Abstract

Abstract-This paper presents a simulation and hardware implementation of a new audio compression scheme based on the fast Hartley transform in combination with a new modified run length encoding. The proposed algorithm consists of analyzing signals with fast Hartley Transform and then thresholding the obtained coefficients below a given threshold which are then encoded using a new approach of run length encoding. The thresholded coefficients are, finally, quantized and coded into binary stream. The experimental results show the ability of the fast Hartley transform to compress audio signals. Indeed, it concentrates the signal energy in a few coefficients and demonstrates the ability of the new approach of run length encoding to increase the compression factor. The results of the current work are compared with wavelet based compression by using objective assessments namely CR, SNR, PSNR and NRMSE. This study shows that the fast Hartley transform is more appropriate than wavelets one since it offers a higher compression ratio and a better speech quality. In addition, we have tested the audio compression system on DSP processor TMS320C6416.This test shows that our system fits with the real-time requirements and ensures a low complexity. The perceptual quality is evaluated with the Mean Opinion Score (MOS).

New Algorithm for Dht and Its Verilog Implementation

“…Four structures are implemented in parallel such that each unit gives 4 coefficients for N=16. For input sequence X = [1,7,8,9,3,2,4,5,1,5,3,4,5,6,7,8], outputs Y = [78, 13.6758, -0.3431, -9.9507, -18, -3.9761, -6, -1.2797, -14, -3.5337, -11.6568, 5.4654, -6, -14.1660, -6, 13.7650] were obtained as seen in the simulation diagram in figure 3 . Total of 45 clock cycles were required to obtain all 16 coefficients at output.…”

Speech signal compression and encryption based on sudoku, fuzzy C-means and threefish cipher

“…(5) (6) (7) where δ(t) = Represents the dirac function, α: represents the angle of rotation (α = α π /2), ƒˊ(t) = The inverse fractional fourier transform.…”

“…In the last few years, numerous different performance methods have been used. The most common techniques in signal compression are: Bousselmi et al [7], present a simulation algorithm of an audio compression scheme based on the fast Hartley transforms that offer a higher compression ratio in combination with a newly modified run-length encoding. Hassan et al [8] suggest the JPEG scheme algorithm, commonly used in digital image compression and digital voice signal compression.…”

A Wavelet-Based Steganographic Method for Text Hiding in an Audio Signal

“…The developed method of steganographic hiding of text information in an audio signal based on the wavelet transform [ 8 ] acquires a deep meaning in the conditions of the use by an attacker of deliberate unauthorized manipulations with a steganocoded audio signal to distort the text information embedded in it; that is, to make its semantic constructions illegible. The main form of these manipulations is the use of various algorithms for compressing the audio signal [ 9 , 10 ], but not to remove its uninformative components, which, according to the human psychophysiological model of sound perception, are beyond the threshold of audibility, and to remove the text information hidden in the audio signal by deliberately introducing distortions by the compression algorithm.…”

“…Thus, using the developed method, it is possible to allow an attacker to re-encode the audio signal with various lossy compression algorithms, but at the same time, the text information embedded in the audio signal maintains integrity. This statement is based on the fact that the current variety of existing compression algorithms [ 9 , 10 , 11 , 12 ] operates according to the same principle, namely, the elimination of the uninformative redundant component of the audio signal. Since the proposed method hides text information at medium frequencies and amplitudes of wavelet coefficients, and because this is its main feature, it can significantly increase the resistance of the stego-system to audio signal compression, taking into account the features of the psychophysiological model of sound perception.…”