很多麦克风阵列时延估计算法在噪声和混响环境下性能都会下降。该文提出一种基于多路线性预测(multi-ple linear prediction,MLP)的时延估计算法。通过传递函数比估计来消除通道间传递函数的非对称性,提高信号相关程度;空间预测技术引入了阵列冗余信息,并以相关系数矩阵作为时延搜索的目标函数,提高时延估计的可靠性。实验结果显示了多路线性预测算法的估计准确率更高,性能更加稳健。与几种经典算法相比,在噪声和混响环境下MLP算法的估计正确率分别提高了5%和30%以上。
提出一种新的通用旁瓣消除器结构,它利用广义奇异值分解(Generalized singular value decomposition,GSVD)技术,通过广义奇异向量的变换间接估计声源到麦克风之间的传递函数。不同噪声环境下的实验结果表明,与现有的各种GSC算法相比,该算法能够更有效地抑制混响和噪声,并且增强后的语音失真最小。
An English speech recognition system was implemented on a chip, called speech system-on-chip （SoC）. The SoC included an application specific integrated circuit with a vector accelerator to improve performance. The sub-word model based on a continuous density hidden Markov model recognition algorithm ran on a very cheap speech chip. The algorithm was a two-stage fixed-width beam-search baseline system with a variable beam-width pruning strategy and a frame-synchronous word-level pruning strategy to significantly reduce the recognition time. Tests show that this method reduces the recognition time nearly 6 fold and the memory size nearly 2 fold compared to the original system, with less than 1% accuracy degradation for a 600 word recognition task and recognition accuracy rate of about 98%.
研究了音频信息处理中一项重要的预处理工作:语音音乐分类。针对语音信号处理中遇到的实际问题,选择合适的音频特征和分类器来对音频数据进行语音和音乐分类。采用二级系统,选择优化低能量率(ModifiedLow Energy Ratio,MLER)以及梅尔频谱倒谱系数(Mel Frequency Cepstral Coefficients,MFCC)作为音频特征,通过贝叶斯分类和混合高斯分类器进行分类。最后,使用上下文分类器对分类结果进行修正。实验结果表明,这种分类方法准确率和速度都较好。
