用于音乐和语音的识别方法不适用于环境音的识别。提出一种基于MFCC(Mel频率倒谱系数)-SVM(支持向量机)的方法,使用特征表示和学习优化共同来实现办公室10种环境音的分类。环境音数据使用的是IEEE Audio and Acoustic Signal Processing...用于音乐和语音的识别方法不适用于环境音的识别。提出一种基于MFCC(Mel频率倒谱系数)-SVM(支持向量机)的方法,使用特征表示和学习优化共同来实现办公室10种环境音的分类。环境音数据使用的是IEEE Audio and Acoustic Signal Processing(AASP)Challenge Dataset下载的标准数据集。在分析和优化SVM参数过程中,通过改变Mel系数参数的个数,充分考虑有效的MFCC特征表示。实验结果表明,使用MFCC特征和SVM分类器,采用5-折交叉验证的测试方法,得到的平均分类准确率可达88.05%,分类效果明显优于默认的MFCC-SVM算法。展开更多
An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking met...An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking method,requires seeking the "a priori" probability of speech absence that is derived by applying microphone input signal and the noise signal based on the estimated value of the "a posteriori" signal-to-noise ratio(SNR).To overcome this problem,first,the optimal values in terms of the perceived speech quality of a variety of noise types are derived.Second,the estimated optimal values are assigned according to the determined noise type which is classified by a real-time noise classification algorithm based on the Gaussian mixture model(GMM).The proposed algorithm estimates the speech absence probability using a noise classification algorithm which is based on GMM to apply the optimal parameter of each noise type,unlike the conventional approach which uses a fixed threshold and smoothing parameter.The performance of the proposed method was evaluated by objective tests,such as the perceptual evaluation of speech quality(PESQ) and composite measure.Performance was then evaluated by a subjective test,namely,mean opinion scores(MOS) under various noise environments.The proposed method show better results than existing methods.展开更多
文摘用于音乐和语音的识别方法不适用于环境音的识别。提出一种基于MFCC(Mel频率倒谱系数)-SVM(支持向量机)的方法,使用特征表示和学习优化共同来实现办公室10种环境音的分类。环境音数据使用的是IEEE Audio and Acoustic Signal Processing(AASP)Challenge Dataset下载的标准数据集。在分析和优化SVM参数过程中,通过改变Mel系数参数的个数,充分考虑有效的MFCC特征表示。实验结果表明,使用MFCC特征和SVM分类器,采用5-折交叉验证的测试方法,得到的平均分类准确率可达88.05%,分类效果明显优于默认的MFCC-SVM算法。
基金Project supported by an Inha University Research GrantProject(10031764) supported by the Strategic Technology Development Program of Ministry of Knowledge Economy,Korea
文摘An improved speech absence probability estimation was proposed using environmental noise classification for speech enhancement.A relevant noise estimation approach,known as the speech presence uncertainty tracking method,requires seeking the "a priori" probability of speech absence that is derived by applying microphone input signal and the noise signal based on the estimated value of the "a posteriori" signal-to-noise ratio(SNR).To overcome this problem,first,the optimal values in terms of the perceived speech quality of a variety of noise types are derived.Second,the estimated optimal values are assigned according to the determined noise type which is classified by a real-time noise classification algorithm based on the Gaussian mixture model(GMM).The proposed algorithm estimates the speech absence probability using a noise classification algorithm which is based on GMM to apply the optimal parameter of each noise type,unlike the conventional approach which uses a fixed threshold and smoothing parameter.The performance of the proposed method was evaluated by objective tests,such as the perceptual evaluation of speech quality(PESQ) and composite measure.Performance was then evaluated by a subjective test,namely,mean opinion scores(MOS) under various noise environments.The proposed method show better results than existing methods.
