An effective analysis of deep learning based approaches for audio based feature extraction and its visualization

作     者：Dhiraj Biswas, Rohit Ghattamaraju, Nischay 

作者机构：CSIR Cent Elect Engn Res Inst CEERI Pilani Rajasthan India Birla Inst Technol & Sci Pilani Rajasthan India 

出 版 物：《MULTIMEDIA TOOLS AND APPLICATIONS》 (多媒体工具和应用)

年 卷 期：2019年第78卷第17期

页      面：23949-23972页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Deep neural networks Convolutional autoencoder VGG Alexnet Audio feature extraction Genre classifiers Audio visualization PCA K-means 

摘      要：Visualizations help decipher latent patterns in music and garner a deep understanding of a song s characteristics. This paper offers a critical analysis of the effectiveness of various state-of-the-art Deep Neural Networks in visualizing music. Several implementations of auto encoders and genre classifiers have been explored for extracting meaningful features from audio tracks. Novel techniques have been devised to map these audio features to parameters that drive visualizations. These methodologies have been designed in a manner that enables the visualizations to be responsive to the music as well as provide unique visual experiences across different songs.