Performance analysis of IEEE 1857.2 lossless audio compression linear predictor algorithm

In addition to commercial consumer market, high quality and multichannel audio has become more relevant in many other fields. More lossless audio compression standards and algorithm are proposed to tackle the problem of reducing the size of a raw audio bitstream without loss of data. This paper has two objectives. First, we aim to review and analyze the performance of the IEEE 1857.2 standard. Focus is on the predictor and the pre-processing block. The predictor utilizes Linear Predictive Coding (LPC) as its main mechanism. The pre-processing block normalizes the error residue of the Linear Predictive encoder. The second objective is to present results from experimenting different wave sound file type inputs. Results are discussed, and comparisons are made to identify the effect on compression ratio of the lossless encoder. As well as this, comparison is made to analyze the entropy flatness of the error residue from the predictor and pre-processing output the predictor order in the linear predictive coding mechanism varies. We concluded that pre-processing block works well to flatten the output at lower predictor order for all the sound types, but works best at improving the residual output for music sound type

Audio Compression using a Modified Vector Quantization algorithm for Mastering Applications

Audio data compression is used to reduce the transmission bandwidth and storage requirements of audio data. It is the second stage in the audio mastering process with audio equalization being the first stage. Compression algorithms such as BSAC, MP3 and AAC are used as standards in this paper. The challenge faced in audio compression is compressing the signal at low bit rates. The previous algorithms which work well at low bit rates cannot be dominant at higher bit rates and vice-versa. This paper proposes an altered form of vector quantization algorithm which produces a scalable bit stream which has a number of fine layers of audio fidelity. This modified form of the vector quantization algorithm is used to generate a perceptually audio coder which is scalable and uses the quantization and encoding stages which are responsible for the psychoacoustic and arithmetical terminations that are actually detached as practically all the data detached during the prediction phases at the encoder side is supplemented towards the audio signal at decoder stage. Therefore, clearly the quantization phase which is modified to produce a bit stream which is scalable. This modified algorithm works well at both lower and higher bit rates. Subjective evaluations were done by audio professionals using the MUSHRA test and the mean normalized scores at various bit rates was noted and compared with the previous algorithms

Audio Compression using a Modified Vector Quantization algorithm for Mastering Applications

Audio data compression is used to reduce the transmission bandwidth and storage requirements of audio data. It is the second stage in the audio mastering process with audio equalization being the first stage. Compression algorithms such as BSAC, MP3 and AAC are used as standards in this paper. The challenge faced in audio compression is compressing the signal at low bit rates. The previous algorithms which work well at low bit rates cannot be dominant at higher bit rates and vice-versa. This paper proposes an altered form of vector quantization algorithm which produces a scalable bit stream which has a number of fine layers of audio fidelity. This modified form of the vector quantization algorithm is used to generate a perceptually audio coder which is scalable and uses the quantization and encoding stages which are responsible for the psychoacoustic and arithmetical terminations that are actually detached as practically all the data detached during the prediction phases at the encoder side is supplemented towards the audio signal at decoder stage. Therefore, clearly the quantization phase which is modified to produce a bit stream which is scalable. This modified algorithm works well at both lower and higher bit rates. Subjective evaluations were done by audio professionals using the MUSHRA test and the mean normalized scores at various bit rates was noted and compared with the previous algorithms