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MUSICAL MODEL TRAINING METHOD, MUSIC CREATION METHOD, DEVICES, TERMINAL AND STORAGE MEDIUM

机译:音乐模型训练方法,音乐创建方法,设备,终端和存储介质

摘要

A musical model training method, comprising: acquiring an MIDI music data set, the MIDI music data set comprising a plurality of MIDI music scores (S11); extracting a feature vector of each of the MIDI scores (S12); inputting the feature vectors into a structured support vector machine for training to obtain a musical model (S13), comprising: constructing a discriminant function f(x; w), x being a feature vector, w being a parameter vector, outputting a data value formula (I) of the maximized discriminant function f(x; w) as a predicted value; calculating the predicted value and a real value according to a preset loss function formula (II), wherein P is a probability distribution of data and is replaced with an empirical risk formula (III) obtained through calculation by training sample data; using an optimization formula (IV) of an SVM to solve the unique parameter vector ω, such that the empirical risk formula (III) by training sample data is zero; obtaining the discrimination function f(x; ω) by means of the solution, and finally outputting a music time sequence. The present invention further provides a music creation method and device, a terminal, and a storage medium. The present invention is the first example of using artificial intelligence to train a musical model, and the trained musical model can improve the feature extraction capability of MIDI music scores.
机译:一种音乐模型训练方法,包括:获取MIDI音乐数据集,所述MIDI音乐数据集包括多个MIDI音乐乐谱(S11);提取每个MIDI乐谱的特征矢量(S12);将特征向量输入到结构化支持向量机中进行训练以获得音乐模型(S13),包括:构造判别函数f(x; w),x为特征向量,w为参数向量,输出数据值最大化判别函数f(x; w)的公式(I)作为预测值;根据预设的损失函数公式(II)计算预测值和实际值,其中P为数据的概率分布,并用训练样本数据计算得出的经验风险公式(III)代替。使用支持向量机的优化公式(IV)求解唯一参数矢量ω,使得训练样本数据得到的经验风险公式(III)为零;通过该解获得判别函数f(x;ω),最后输出音乐时序。本发明还提供一种音乐创作方法和设备,终端和存储介质。本发明是使用人工智能训练音乐模型的第一个例子,并且训练后的音乐模型可以提高MIDI音乐乐谱的特征提取能力。

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