Scalable Audio Coding using the Oversampled Discrete Wavelet Transform and Temporal Masking

School of Electrical Engineering and Telecommunications The University of New South Wales Sydney, NSW 2052, Australia
Abstract-In this paper, we propose a low-complexity scalable audio coding algorithm using an over sampled discrete wavelet transform (ODWT). The aliased free coefficients obtained are then used to accurately calculate the forward temporal masking threshold. The algorithm based on the bandwidth scalability, uses bits per sub-band and bits per frame, as outlined in this paper. Subjective tests conforming to ITU-R BS.1116 for audio signals show that the audio signals coded at 32 and 64 kbits/s using an ODWT with temporal masking offered a better audio quality compared to the critically sampled DWT.
I.
A. Overview The block diagram of the scalable audio coder used in this work is shown in Figure 1. Eight level decompositions of the over sampled discrete wavelet transform (ODWT) are performed using the "a Trous" algorithm [4]. The wavelet coefficients obtained from the ODWT are then used for masking threshold calculations. The critically sampled wavelet coefficients are available as a subset of the over sampled wavelet coefficients and therefore the increase of computational load from the ODWT only occurs at the encoder, thus the decoder is unaffected.
efficient embedded audio coding (EAC) algorithm outperforms the audio compression standards [2]. However, in all these audio compression algorithms, an accurate calculation of auditory masking threshold is essential for efficient compression. Wavelets are especially suitable for scalable audio coding because their multi resolution property can be directly applied to bandwidth scalability [1]. Many existing audio coders use a critically sampled DWT for the decomposition of audio signals and these coders normally perform the calculation of masking thresholds directly on the aliased coefficients produced by the DWT. Recently, an over sampled DWT has been proposed in [2] to calculate the masking threshold more accurately and to produce an improvement in the audio quality that was coded at approximately 96 kbps. Moreover, temporal masking has been found to reduce the bit rate while maintaining transparent quality [2, 3]. In this paper, we propose a low-complexity scalable audio coding algorithm using the ODWT incorporating a temporal masking model combined with a simultaneous masking model to achieve an efficient compression.
II.
SCALABLE AUDIO CODER
Keywords-scalable audio coding, oversampled discrete wavelel transform, auditory masking, temporal masking
INTRODUCTION The basic objective in audio coding is to achieve transparent quality in the reconstructed audio signal at a minimal bit rate. Motivation for this emerges from various applications, such as audio over Internet and digital broadcasting that has bandwidth limitation in transmitting the digital data. The challenge is to get a better audio quality from an audio signal sent through a limited-bandwidth channel, where different applications may require different bandwidths. In these situations scalable audio coders such as MPEG-4 provide an efficient solution [1]. On the other hand, the highly
ICICS 2005
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191
of 2. This aliasing will be cancelled out in the decoder, but due to the fact that masking calculation is performed before the decoding process using these aliased coefficients, it results in an inaccurate masking threshold. The ODWT provides aliasfree wavelet coefficients and as a result, an accurate masking threshold is obtained. The masking threshold is normally calculated using subband signal power. Suppose that we define Pos and PCs as the energy of the wavelet coefficient with maximum energy in each sub-band using the ODWT and CSDWT, respectively. Figure 2 shows the possibility of two conditions of Pos and Pcs, that is Pos > Pcs and Pos =PCs where five audio materials were used to verify these conditions. It can be concluded that a more accurate signal power can be calculated using the aliased free wavelet coefficients obtained in ODWT than using CSDWT. Results show that this is true for almost 90O on average [6].
B.
Oversampled Discrete Wavelet Transform The critically sampled DWT is a very powerful tool in audio coding, as it results in a compact, yet complete representation of an audio signal [3]. In addition, it also allows sub-band decomposition that approximates the critical band structure of the human auditory system. The main drawback in the critically sampled DWT is that aliasing is present in the wavelet coefficients, as a result of down sampling by a factor