Capturing the Temporal Domain in Echonest Features for Improved Classification Effectiveness

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• 作者：Alexander Schindler (17) (18)
  Andreas Rauber (17)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：1
• 期：1
• 页码：214-227
• 全文大小：381 KB
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• 作者单位：Alexander Schindler (17) (18)
  Andreas Rauber (17)
  
  17. Department of Software Technology and Interactive Systems, Vienna University of Technology, Vienna, Austria
  18. Intelligent Vision Systems, AIT Austrian Institute of Technology, Vienna, Austria
  
• ISSN：1611-3349

文摘

This paper proposes Temporal Echonest Features to harness the information available from the beat-aligned vector sequences of the features provided by The Echo Nest. Rather than aggregating them via simple averaging approaches, the statistics of temporal variations are analyzed and used to represent the audio content. We evaluate the performance on four traditional music genre classification test collections and compare them to state of the art audio descriptors. Experiments reveal, that the exploitation of temporal variability from beat-aligned vector sequences and combinations of different descriptors leads to an improvement of classification accuracy. Comparing the results of Temporal Echonest Features to those of approved conventional audio descriptors used as benchmarks, these approaches perform well, often significantly outperforming their predecessors, and can be effectively used for large scale music genre classification.