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Record link: 
http://hdl.handle.net/20.500.12708/39105
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Title: 
Parallel convolutional neural networks for music genre and mood classification
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Citation: 
Lidy, T., & Schindler, A. (2016). Parallel convolutional neural networks for music genre and mood classification. http://hdl.handle.net/20.500.12708/39105
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Publication Type: 
Report - Research Report
en
Authors: 
Lidy, Thomas 
Schindler, Alexander 
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Organisational Unit: 
E194-01 - Forschungsbereich Information und Software Engineering 
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Date (published): 
2016
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Number of Pages: 
4
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Keywords: 
Audio; Classification; Deep learning; Neural networks; Music; Convolutional Neural Networks
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Abstract: 
Our approach to the MIREX 2016 Train/Test Classification Tasks for Genre, Mood and Composer detection is based on an approach combining Mel-spectrogram transformed audio and Convolutional Neural Networks (CNN). We utilize two different CNN architectures, a sequential one, and a parallel one, the latter aiming at capturing both temporal and timbral information in two different pipelines, which are merged on a later stage. In both cases, the crucial CNN parameters such as filter kernel sizes and pooling sizes were carefully chosen after a range of experiments.
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Science Branch: 
Informatik
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