Evaluation of Audio Compression Artifacts

This paper deals with subjective evaluation of audio-coding systems. From this evaluation, it is found that, depending on the type of signal and the algorithm of the audio-coding system, different types of audible errors arise. These errors are called coding artifacts. Although three kinds of artifacts are perceivable in the auditory domain, the author proposes that in the coding domain there is only one common cause for the appearance of the artifact, inefficient tracking of transient-stochastic signals. For this purpose, state-of-the art audio coding systems use a wide range of signal processing techniques, including application of the wavelet transform, which is described here.

Scalable and perceptual audio compression

This thesis deals with scalable perceptual audio compression. Two scalable perceptual solutions as well as a scalable to lossless solution are proposed and investigated. One of the scalable perceptual solutions is built around sinusoidal modelling of the audio signal whilst the other is built on a transform coding paradigm. The scalable coders are shown to scale both in a waveform matching manner as well as a psychoacoustic manner. In order to measure the psychoacoustic scalability of the systems investigated in this thesis, the similarity between the original signal\u27s psychoacoustic parameters and that of the synthesized signal are compared. The psychoacoustic parameters used are loudness, sharpness, tonahty and roughness. This analysis technique is a novel method used in this thesis and it allows an insight into the perceptual distortion that has been introduced by any coder analyzed in this manner

AZIP, audio compression system: Research on audio compression, comparison of psychoacoustic principles and genetic algorithms

The purpose of this project is to investigate the differences between psychoacoustic principles and genetic algorithms (GA0). These will be discussed separately. The review will also compare the compression ratio and the quality of the decompressed files decoded by these two methods

A review of lossless audio compression standards and algorithms

Over the years, lossless audio compression has gained popularity as researchers and businesses has become more aware of the need for better quality and higher storage demand. This paper will analyse various lossless audio coding algorithm and standards that are used and available in the market focusing on Linear Predictive Coding (LPC) specifically due to its popularity and robustness in audio compression, nevertheless other prediction methods are compared to verify this. Advanced representation of LPC such as LSP decomposition techniques are also discussed within this paper

Digital recording of performing arts: formats and conversion

B

Lossless audio compression of speech and voice

Data compression plays an important role in modern telecommunication, entertainment, computing and networking areas because the capacities of media for storage and bandwidth for transmission are not growing proportionally with the rapid demand for multimedia data. Speech and voice compression is one of applications in this field. Although there are a lot of techniques used in speech coding, new algorithms need to be developed to achieve better performance. Our research focus is on lossless speech and voice compression using wavelet transform, prediction, and Rice coDing These techniques have some properties, such as, very fast computation and easily exploiting the redundancy in the speech signal. By taking these advantages, we can reduce the number of bits required to represent the audio signal and get better lossless audio compression. In this thesis, basic concepts and properties of these techniques will be displayed and a new loseless algorithm based on these methods is presented as well. The test results of the new algorithm are shown and some analyses are given

Audio Compression Using DCT and DWT Techniques

In today’s world multimedia files are used, storage space required for these files is more and sound files have no option so ultimate solution for this is compression. Compression is nothing but high input stream of data converted into smaller size. Speech Compression is a field of digital signal processing that focuses on reducing bit-rate of speech signals to enhance transmission speed and storage requirement of fast developing multimedia. In many applications, such as the design of multimedia workstations and high quality audio transmission and storage, the goal is to achieve transparent coding of audio and speech signals at the lowest possible data rates. Therefore, the transmission and storage of information becomes costly. However, if we can use less data, both transmission and storage become cheaper. Further reduction in bit rate is an attractive proposition in applications like remote broadcast lines, studio links, satellite transmission of high quality audio and voice over internet. This paper explores a transform based methodology for compression of the speech signal. In this methodology, different transforms such as Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT) are exploited. A comparative study of performance of different transforms is made in terms of Signal-to-noise ratio (SNR) and  Peak signal-to-noise ratio (PSNR). The mean compression ratio is also calculated for all the methods and compared. The simulation results included illustrate the effectiveness of these transforms in the field of data compression. Keywords-DCT (Discrete cosine transform), DWT (Discrete wavelet transform), Quantization Compression Factor (CF), Signal to Noise ratio (SNR)