华东理工大学学报（自然科学版）2017，Vol.43Issue(2)：241-247,7.DOI:10.14135/j.cnki.1006-3080.2017.02.014
基于降噪自动编码器特征学习的音乐自动标注算法
Feature Learning for Music Auto-Tagging Using Denoising Autoencoder
摘要
Abstract
At present,the models used in music auto-tagging are mostly hand-engineered,so the choice of the optimal feature is always difficult.We propose an unsupervised feature learning algorithm,which can automatically learn the underlying structure of feature without prior knowledge.The algorithm is achieved in three stages.The preprocessing stage extracts the chroma-frequency spectrogram,and reduces the dimensionality via PCA whitening.The second stage applies the denoising autoencoder to the reduced feature in an unsupervised manner,and aggregates a new feature vector by max-pooling function and averaging.The last stage maps the feature vector to song labels by pre-trained multilayer perceptron (MLP) in a supervised manner.The result based on the Magnatagatune and GTZAN datasets shows that our algorithm improves the accuracy of music auto-tagging to some degree.关键词
深度学习/音乐自动标注/降噪自动编码器/多层感知机Key words
deep learning/music auto-tagging/denoising autoencoder/multilayer perceptron分类
信息技术与安全科学引用本文复制引用
黎鹏,陈宁..基于降噪自动编码器特征学习的音乐自动标注算法[J].华东理工大学学报（自然科学版）,2017,43(2):241-247,7.基金项目
国家自然科学基金(61271349) （61271349）
