Audio Compression Using Graph-based Transform

Abstract

Graph-based Transform is one of the recent transform coding methods which has been used in some stateof-arts for data decorrelation. In this paper, we propose a Graph-based Transform (GT) for audio compression. In this method we introduce a proper graph structure for audio. Then audio frames are projected onto an orthogonal matrix consisting of eigenvectors of the introduced graph matrix, leading to sparse coefficients. The results show that the proposed method can decorrelate audio signal better than other transform methods like Discrete Cosine Transform (DCT) and Walsh-Hadamard Transform (WHT).

Robust Audio Watermarking Using Graph-based Transform and Singular Value Decomposition

“…This graph should de-correlate the audio frames and provide some large coefficients at the first samples, while the rest of the coefficients (which are the majority of coefficients) should be very small or even zero. We showed in [8] that, Figure 4 is an appropriate graph structure for the audio signals and provides a better compression rate than the one used through DCT. Following our work [8], we use the same structure in the present work.…”

“…We showed in [8] that, Figure 4 is an appropriate graph structure for the audio signals and provides a better compression rate than the one used through DCT. Following our work [8], we use the same structure in the present work. Figure 2, shows the appropriate graph structure for a frame with a length of 8 samples.…”

“…One of the applications of graphical processing is the graph-oriented conversion, which is often used to compress information [6,7]. A method for audio compression by Graph-based Transform is reported in [8], which proposes this method over a popular conventional method, namely DCT (discrete cosine transformation). The main idea of this work is to use this method to add the watermark data to parts of the audio signal which are resistant to compression.…”

Effective Audio Compression using Enhanced Encoding Method

“…Thus, we present a legitimate map structure for audio. It appears that the result of the proposed technique beats the ordinary transformation techniques in speed and compression ratio like Discrete Cosine Transform (DCT) and Walsh-Hadamard Transform (WHT) in decorrelation of the audio signals [9]. The motivation of our work is rooted in addressing the formidable obstacles posed by the exponential surge in big data and the constant evolution of technological landscapes, particularly in the domain of audio compression.…”