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Model-Based Speech Signal Coding Using Optimized Temporal Decomposition for Storage and Broadcasting Applications

机译:基于时间分解的基于模型的语音信号编码在存储和广播应用中的应用

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摘要

A dynamic programming-based optimization strategy for a temporal decomposition (TD) model of speech and its application to low-rate speech coding in storage and broadcasting is presented. In previous work with he spectral stability-based event localizing (SBEL) TD algorithm, the event localization was performed based on a spectral stability criterion. Although this approach gave reasonably good results, there was no assurance on the optimality of the event locations. In the present work, we have optimized the event localizing task using a dynamic programming-based optimization strategy. Simulation results show that an improved the event localizing task using a dynamic programming-based optimization strategy. Simulation results show that an improved TD model accuracy can be achieved. A methodology of incorporating the optimized TD algorithm within the standard MELP speech coder for the efficient compression of speech spectral information is also presented. The performance evaluation results revealed that the proposed speech coding scheme achieves 50%-60% compression of speech spectral information with negligible degradation in the decoded speech quality.
机译:提出了一种基于动态规划的语音时间分解模型优化策略及其在存储和广播中低速语音编码中的应用。在以前的基于频谱稳定性的事件定位(SBEL)TD算法的工作中,事件定位是基于频谱稳定性标准执行的。尽管此方法给出了相当不错的结果,但不能保证事件位置的最佳性。在当前的工作中,我们已使用基于动态编程的优化策略优化了事件定位任务。仿真结果表明,使用基于动态编程的优化策略改进了事件定位任务。仿真结果表明,可以提高TD模型的精度。还提出了将优化的TD算法并入标准MELP语音编码器中以有效压缩语音频谱信息的方法。性能评估结果表明,所提出的语音编码方案实现了对语音频谱信息的50%-60%压缩,而解码语音质量的下降可忽略不计。

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