Technical Reports - Query Results
Your query term was 'number = 2011-20'1 report found
- OFAI-TR-2011-20 (
343kB PDF file)Music Similarity Estimation with the Mean-Covariance Restricted Boltzmann Machine
- Jan Schlueter, Christian Osendorfer
- Existing content-based music similarity estimation
methods largely build on complex hand-crafted feature extractors,
which are difficult to engineer. As an alternative, unsupervised
machine learning allows to learn features empirically from
data. We train a recently proposed model, the mean-covariance
Restricted Boltzmann Machine, on music spectrogram excerpts
and employ it for music similarity estimation. In k-NN
based genre retrieval experiments on three datasets, it clearly
outperforms MFCC-based methods, beats simple unsupervised
feature extraction using k-Means and comes close to the stateof-
the-art. This shows that unsupervised feature extraction poses
a viable alternative to engineered features.
Keywords: Music Information Retrieval, Music Similarity, Boltzmann Machine
- Existing content-based music similarity estimation
methods largely build on complex hand-crafted feature extractors,
which are difficult to engineer. As an alternative, unsupervised
machine learning allows to learn features empirically from
data. We train a recently proposed model, the mean-covariance
Restricted Boltzmann Machine, on music spectrogram excerpts
and employ it for music similarity estimation. In k-NN
based genre retrieval experiments on three datasets, it clearly
outperforms MFCC-based methods, beats simple unsupervised
feature extraction using k-Means and comes close to the stateof-
the-art. This shows that unsupervised feature extraction poses
a viable alternative to engineered features.