摘要：

现有的开环模式选择算法依赖信号分类的准确率,但多数情况下准确率较低,造成开环模式下编码音质较差。为此,提出一种改进的基于神经网络的开环模式选择算法。使用神经网络替换原开环模式选择的决策树算法,拟合闭环模式选择结果进行训练得到模式选择分类器,按 照闭环模式选择的逻辑过程,运用神经网络预测输入的信号,在ACELP256和TVC256两种编码模式的信噪比取代编码尝试计算得到的信噪比。实验结果表明,与原AVS-P10开环选择方法相比,提出的2种模式在语音分类准确率上分别提升5.96%和18.07%,在音乐分类准确率上分别 提升3.84%和20.29%,其主客观编码音质评测明显提升。

关键词: 神经网络, 先进音视频编码, 模式选择, 特征选择, 信号分类, 信噪比估计

Abstract:

The existing open-loop mode selection algorithm relies on the accuracy of signal classification,but in most cases the accuracy is low,resulting in poor coding quality in open-loop mode.Therefore an improved neural network based open-loop mode selection algorithm is proposed.The decision tree algorithm of the original open-loop mode selection is replaced by the neural network,and the closed-loop mode selection is selected for training to obtain the mode selection classifier.According to the logic process of the closed-loop mode selection,the neural network is used to predict the input signal,and the two codes are ACELP256 and TVC256.The signal to noise ratio of the mode replaces the signal-to- noise ratio that the coding attempt is calculated.Experimental results show that the accuracy of the two methods is 5.96% and 18.07%,respectively,and the accuracy of music classification is 3.84% and 20.29%,the performance of subjective and objective tone has high improvement.

Key words: neural network, advanced audio and video coding, mode selection, feature selection, signal classification, Signal to Noise Ratio(SNR) estimation

中图分类号: 

• TP391