Research On Key Techniques Of Mobile Audio Coding And Decoding

The changeable and limited bandwidth and limited computing and storage resources have brought new challenges to mobile audio compression. This thesis made researches on the bandwidth extension, speech/audio hybrid coding, and lattice vector quantization.1. This thesis first proposed a single mode of speech/audio coding schemes -Modified Transform Coding Excitation (MTCX), which solved the problem of complex audio signal compression. Then the quantization of Immittance Spectral Frequencies (ISF) and long prediction coding were studied.(1) Taking into account the Inter and Intra frame correlation of ISF coefficients, this thesis proposed a new switched dual prediction mixed vector quantization algorithm.Based on the the multi-stage and split vector quantization, two predictive coefficients for strong and weak correlation of ISF parameters were used respectively to reduce correlation of ISF parameters. The experimental results showed that the algorithm performance was better than that of mixed vector quantization scheme of AMR-WB+ standard.(2) Harmonic components would be extended to the high frequency of the long-term prediction excitation signal without selection. However, if the harmonic character of high-frequency signal was not strong, such non-selective extension would reduce the performance of long term prediction algorithm. The Inter-band Waveform Cross-Correlation based Adaptive long term prediction search algorithm was proposed. Correspond to the long term excitation signal, the correlation between the high-frequency part of long term excitation signal and the predictive error signal was calculated, and in accordance with relevant result to determine whether the long term excitation signal should be filtered by a low pass filter to filtering out potential non-cyclical high-frequency harmonic components. Experimental results show that the algorithm can effectively improve the segment SNR, and compared to the traditional multi-path choice algorithm adopted by AMR-WB+ standard, the proposed algorithm had less computational complexity and better performance.2. Bandwidth extension could significantly improve the compression efficiency, which could reduce an half coding bit-rate under the premise of the same quality. Based on the human ear perceptual character that human ears could not keep up with the changes and distinguish the fine structure of the spectrum but can only sense the energy spectrum envelope and the perception of high-frequency signal, a perceptual bandwidth extension algorithm was studied. The algorithm only extracted the energy gain and spectrum envelope parameters, which were important parameters for the perception of high frequency reconstruction. It is difficult to smooth the high-frequency reconstructed spectrum in traditional bandwidth extension algorithms. A synthesis filter zero impulse response matching algorithm was studied which usesd no additional bits to resolve the problem of smoothing the reconstruction signal spectrum. The experimental results showed that the algorithm had an advantage of a higher compression ratio (encoding bit rate 0.8 kbps) and the good quality.3. Lattice vector quantization with low computational complexity, low storage and higher quantization accuracy was a key technology in mobile audio coding. However, the traditional lattice vector quantization algorithm would cause large quantization error for outsides under low bit rate. To address this problem, this thesis proposed a scalable Voronoi lattice vector quantization algorithm. The quantization algorithm composed of two steps:the first was to scale the basic codebook and quantize the input vector with the scaled basic codebook vector, and the second was to use a series of Voronoi Vectors to quantize error of the first step. The series of Voronoi Vectors came from the extension Voronoi codebooks by expanding the one order Voronoi codebook step by step. The quantization algorithm not only addressed the lattice vector quantization problem for the outside. Experimental results show that the quantization performance of the proposed algorithm corresponded to that of variable rate lattice vector quantization technology of AMR-WB+ standard, and had less storage space.Finally, this paper summarized the research achievement and looked into the future research content.