/pdf/advances-in-perceptual-stereo-audio-coding-using-linear-46sl1n1aoj.pdf

Advances in perceptual stereo audio coding using linear prediction techniques

High audio data compression can be achieved by removing irrelevant signal information that is not detectable by even a well-trained or sensitive listener. Contemporary audio coding schemes like MP3, AAC, and Ogg Vorbis identify the irrelevant information during signal analysis by incorporating into the coder several psychoacoustic principles, including absolute hearing thresholds, critical band analysis, simultaneous masking, and temporal masking (Painter and Spanias, 2000). Masking is the process of removing faint but normally audible sound signals that are rendered inaudible as they are very close in frequency to or have much smaller amplitudes than surrounding sounds. Numerous studies have been conducted on genetic algorithms, which solve problems by modeling the Darwinian evolution. The algorithms have been recently applied to audio coding with some success (Galos et al., 2003). To achieve audio compression, genetic algorithms analyze a large number of sound files to determine the chunks that are most likely to contain irrelevant signals. The combination of the irrelevant chunks, form a solution which will be used to compress any sound files. We present in this paper a study of the comparison of applying psychoacoustic principles and genetic algorithms to compress audio signals. We developed a coder to perform the experiment, where like most well-known audio coders, Huffman coding is used to handle lossless compression and modified discrete cosine transform (MDCT) is used to transform the time-domain signals to the frequency domain. The results are compared using signal-to-noise ratios (SNRs) and subjective testing, where eighteen subjects (who are students in CSUSB) are asked to listen and rate the decompressed files by the two methods.

Comparison of psychoacoustic principles and genetic algorithms in audio compression

The time-frequency tiling, bit allocation and the quantizer of most perceptual coding algorithms is either fixed or controlled by a perceptual mode. The large variety of existing audio signals, each exhibiting different coding requirements due to their different temporal and spectral fine-structure suggests to use a signal-adaptive algorithm. The framework which is described in this is paper makes use of a signal-adaptive wavelet filterbank which allows to switch any node of the wavelet-packet tree individually. Therefore each subband can have an individual time- segmentation and the overall time-frequency tiling can be adapted to the signal using optimization techniques. A rate- distortion optimality can be defined which will minimize the distortion for a given rate in every subband, based on a perceptual model. Due to the additivity of the rate and distortion measure over disjoint covers of the input signal, an overall cost function including the switching cost for the filterbank switching can be defined. By the use of dynamic programming techniques, the wavelet-packet tree can be pruned base don a top-down or bottom-up 'split-merge' decision in every node of the wavelet-tree. Additionally we can profit form temporal masking due to the fact that each subband can have an individual segmentation in time without introducing time domain artifacts such as pre-echo distortion.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Audio coding based on rate distortion and perceptual optimization

Improving subband spectral estimation using modified AR model

Most audio compression formats are based on the idea of low bit rate transparent encoding. As these types of audio signals are starting to migrate from portable players with inexpensive headphones to higher quality home audio systems, it is becoming evident that higher bit rates may be required to maintain transparency. We propose a novel method that enhances low bit rate encoded audio segments by applying multiband audio resynthesis methods in a postprocessing stage. Our algorithm employs the highly flexible Generalized Gaussian mixture model which offers a more accurate representation of audio features than the Gaussian mixture model. A novel residual conversion technique is applied which proves to significantly improve the enhancement performance without excessive overhead. In addition, both cepstral and residual errors are dramatically decreased by a feature-alignment scheme that employs a sorting transformation. Some improvements regarding the quantization step are also described that enable us to further reduce the algorithm overhead. Signal enhancement examples are presented and the results show that the overhead size incurred by the algorithm is a fraction of the uncompressed signal size. Our results show that the resulting audio quality is comparable to that of a standard perceptual codec operating at approximately the same bit rate.

/pdf/quality-enhancement-of-compressed-audio-based-on-statistical-1x8v4ai9rt.pdf

Quality Enhancement of Compressed Audio Based on Statistical Conversion

A novel audio coding algorithm is proposed where the warped-linear prediction (WLP) technique is employed to construct a perceptual pre- and post-filter for subband audio coding. A modified signal-to-mask ratio (SMR) calculation is given for subband coding of the WLP residuals of audio signals. The concept of perceptual entropy (PE) is extended to subband coding, resulting in the subband perceptual entropy (SPE), which gives a short time estimate of the lowest possible bit rate for transparent subband audio coding. Two WLP models with frequency responses approximating the spectral shape of the masking threshold are investigated and it is found that the residual signals of both models contain less SPE compared with that of the original audio signals. Subjective tests show that the proposed audio codec operating at 56 kbps has a perceptual quality comparable to MPEG-1 audio Layer II operating at 64 kbps.

Rongshan Yu

Papers

A warped linear-prediction-based subband audio coding algorithm