【摘要】 音频信号作为多媒体信息的重要载体之一,为满足人们对信息知识的获取提供了有效途径.为了提高音频分类的精度,提出一种将音频信号的梅尔频率倒谱系数(Mel frequency cepstrum coefficient,MFCC)参数作为特征向量,采用基于改进型传输函数的误差反向传播神经(back propagation,BP)网络模型对6种音频进行分类.实验证明,该方法在音频分类精度方面性能良好,改进的传输函数具有收敛速度快的优点.相对于传统BP算法,该方法不仅缩短了训练时间,而且进一步提高了分类精度,其分类准确率达到90%以上.更多还原

【Abstract】 Audio is an important medium that carries substantial information to meet human needs.To improve accuracy of audio classification,we propose a new algorithm with Mel frequency cepstrum coefficient(MFCC) parameters as the feature vectors,and use a back propagation(BP) neural network model based on improved transfer function to classify six types of audio signals.Experiments show that the proposed algorithm has good performance and the improved transfer function converges faster that the traditional BP algorithm.It can reduce training time,and improve classification accuracy up to more than 90%.更多还原