School of Music and Dance of Changzhi University, Changzhi, Shanxi 046011, China.
Department of Intelligence and Automation of Taiyuan University, Taiyuan, Shanxi 030032, China.
Comput Intell Neurosci. 2022 Jun 27;2022:5457376. doi: 10.1155/2022/5457376. eCollection 2022.
Semiautomated digital creation is increasingly important in the manipulation of electronic music. How to realize the learning of local effective features of audio data is a difficult point in the current research field. Based on recurrent neural network theory, this paper designs a semiautomatic digital creation system for electronic music for digital manipulation and genre classification. The recurrent neural network improves the transmission of electronic music information between the input and output of the network by adopting dense connections consistent with DenseNet and adopts an inception-like structure for the autonomous selection of effective recursive nuclear electronic music categories. In the simulation process, the prediction method based on semiautomatic digital audio clips is also adopted, which pays more attention to the learning of local effective features of audio data, which gives the model the ability to create audio samples of different lengths and improves the model's support for creative tasks in different scenarios. It includes the determination of the number of neurons, the selection of the function of neurons, the determination of the connection method, and the specific learning algorithm rules, and then the training samples are formed. The experimental results show that the recurrent neural network exhibits powerful feature extraction ability and classification ability of music information. The 10-fold cross-validation on GTZAN dataset and ISMIR2004 dataset has obtained 88.7% and 87.68%, surpassing similar ones. The model has reached a leading level. After further use of the MSD (Million Song Dataset) dataset for pre-semiautomatic training, the model effect has been further greatly improved. The accuracy rate on the dataset has been increased to 91.0% and 89.91%, respectively, which has improved the semiautomatic number and creative advancement.
半自动化数字创作在电子音乐的处理中变得越来越重要。如何实现对音频数据局部有效特征的学习是当前研究领域的一个难点。本文基于循环神经网络理论，设计了一种用于电子音乐数字操作和类型分类的半自动化数字创作系统。该循环神经网络通过采用与 DenseNet 一致的密集连接，改善了网络输入和输出之间的电子音乐信息传递，并采用类似 inception 的结构，自主选择有效的递归核电子音乐类别。在模拟过程中，还采用了基于半自动数字音频剪辑的预测方法，该方法更注重对音频数据局部有效特征的学习，使模型具有生成不同长度音频样本的能力，提高了模型对不同场景下创意任务的支持能力。它包括神经元数量的确定、神经元功能的选择、连接方法的确定和具体学习算法规则的确定，然后形成训练样本。实验结果表明，循环神经网络具有强大的音乐信息特征提取能力和分类能力。在 GTZAN 数据集和 ISMIR2004 数据集上进行的 10 倍交叉验证，分别获得了 88.7%和 87.68%的准确率，超过了类似模型。该模型已经达到了领先水平。进一步使用 MSD（百万歌曲数据集）数据集进行半自动预训练后，模型效果得到了进一步的显著提高。在数据集上的准确率分别提高到了 91.0%和 89.91%，提高了半自动数量和创作水平。
