Comparative Analysis of Speech Compression Algorithms with Perceptual and LP based Quality Evaluations

Abstract

Speech compression is one of the leading vicinity of digital signal processing that spotlight on dipping the bit rate of speech signals for transmission and storage devoid of considerable loss of quality. In past decades many speech coding techniques have been proposed for speech analysis. This paper attempts to assess and compare two compression techniques on speech signals. To execute this idea we have chosen two low bit rate and widely used speech analysis methods called VELP and MELP. The performances of both are evaluated by performing objective quality tests including PESQ, IS and CEP. Similar speech files are tested with both coders. The objective assessments show that at low bit rate the MELP shows better performance as compared to VELP.

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.…”