Abstract
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.
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Schindler, A., Rauber, A. (2014). Capturing the Temporal Domain in Echonest Features for Improved Classification Effectiveness. In: Nürnberger, A., Stober, S., Larsen, B., Detyniecki, M. (eds) Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation. AMR 2012. Lecture Notes in Computer Science(), vol 8382. Springer, Cham. https://doi.org/10.1007/978-3-319-12093-5_13
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DOI: https://doi.org/10.1007/978-3-319-12093-5_13
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