Fisher-score计算MFCC特征每一阶的Fisher比代码
时间: 2023-03-14 18:02:55 浏览: 51
Fisher比可以用来计算MFCC特征的每一阶的Fisher分数,它可以通过计算两个类别之间的方差和协方差来评估特征的有效性。代码如下:def fisher_score(data, labels):
classes = np.unique(labels)
if len(classes) == 1:
return 0
elif len(classes) == 2:
n1, n2 = len(labels[labels==classes[0]]), len(labels[labels==classes[1]])
mean1, mean2 = np.mean(data[labels==classes[0]], axis=0), np.mean(data[labels==classes[1]], axis=0)
cov1, cov2 = np.cov(data[labels==classes[0]], rowvar=False), np.cov(data[labels==classes[1]], rowvar=False)
cov_pooled = (n1 * cov1 + n2 * cov2) / (n1 + n2)
denom = np.sqrt(np.diag(np.dot(np.dot((mean1 - mean2), np.linalg.inv(cov_pooled)), (mean1 - mean2).T)))
fs = np.dot((mean1 - mean2), np.linalg.inv(cov_pooled)) / denom
return fs
else:
raise ValueError('Fisher score is only defined for two classes.')
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