基于标签深度分析的音乐自动标注算法

Music Auto-tagging Algorithm Based on Deep Analysis on Labels

尽管深度神经网络算法在标签自动标注领域已取得一定的成果,但对于包含大量噪声标签的真实音乐数据集仍存在自动标注效果差的问题.为此,文中通过对音乐标签进行表示学习,挖掘音乐标签与音频特征之间的潜在关系,提出了基于标签深度分析的音乐自动标注算法.该算法先通过多层级卷积网络提取音频特征,再通过音乐标签向量的表示学习来降低噪声数据对音乐自动标注网络的不良影响.在真实音乐标注数据集上的实验结果表明,该算法能取得更高的平均受试者特征曲线下面积,标注效果优于其他自动标注算法.

Deep neural network algorithms have made breakthroughs in automatic labeling tasks,but it is still hard to solve the noise data problem in real music dataset.A music auto-tagging algorithm based on deep analysis on labels(DAL)which captures the potential relationship between audio features and music tags was proposed.The algorithm first extracts the audio features through a multi-level convolutional network,and then learn the vector representation of music tags to reduce the adverse effects of noise data.The experimental results show that the proposed algorithm can achieve higher mean area under receiver operating characteristic curve(AUROCC)and outperform other auto-tagging methods.