阅读说明：本技术 基于约束半非负矩阵分解的声音分类方法、装置及介质 (Sound classification method, device and medium based on constrained semi-nonnegative matrix factorization ) 是由 韩威 周松斌 李昌 刘忆森 刘伟鑫 于 2018-12-07 设计创作，主要内容包括：一种基于约束半非负矩阵分解的声音分类方法、装置及介质,该声音分类包括：将训练声音数据样本和测试声音数据样本表示为半非负矩阵(S1)；根据半非负矩阵构建类别约束矩阵,并根据半非负矩阵构建稀疏约束矩阵(S2)；在类别约束和稀疏约束下,对半非负矩阵进行约束半非负矩阵分解,得到对应的系数矩阵；将系数矩阵中对应于训练声音数据样本的低维表示以及训练声音数据样本的类别信息作为训练数据,对分类模型进行训练得到分类器(S3)；将系数矩阵中对应于测试声音数据样本的低维表示输入分类器,输出测试声音数据样本的分类结果(S4)。该方法有效利用了训练声音数据样本的类别信息并使得降维后的低维表示具有稀疏性,从而得到更具区分性的样本低维表示,提高了声音数据分类方法的正确率。(A sound classification method, device and medium based on constrained semi-nonnegative matrix factorization, the sound classification includes: representing the training sound data samples and the test sound data samples as a semi-non-negative matrix (S1); constructing a category constraint matrix according to the semi-nonnegative matrix, and constructing a sparse constraint matrix according to the semi-nonnegative matrix (S2); under category constraint and sparse constraint, carrying out constraint semi-nonnegative matrix decomposition on the semi-nonnegative matrix to obtain a corresponding coefficient matrix; training a classification model to obtain a classifier by using the low-dimensional representation corresponding to the training voice data samples in the coefficient matrix and the class information of the training voice data samples as training data (S3); the low-dimensional representation corresponding to the test sound data sample in the coefficient matrix is input to the classifier, and the classification result of the test sound data sample is output (S4). The method effectively utilizes the class information of the training sound data samples and enables the low-dimensional representation after dimensionality reduction to have sparsity, so that more distinctive sample low-dimensional representation is obtained, and the accuracy of the sound data classification method is improved.)