Thomas Lidy
Cited by
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Evaluation of feature extractors and psycho-acoustic transformations for music genre classification
T Lidy, A Rauber
ISMIR, 34-41, 2005
Structured visual markers for indoor pathfinding
M Kalkusch, T Lidy, N Knapp, G Reitmayr, H Kaufmann, D Schmalstieg
The First IEEE International Workshop Agumented Reality Toolkit,, 8 pp., 2002
Experimenting with musically motivated convolutional neural networks
J Pons, T Lidy, X Serra
2016 14th international workshop on content-based multimedia indexing (CBMI …, 2016
Improving Genre Classification by Combination of Audio and Symbolic Descriptors Using a Transcription Systems.
T Lidy, A Rauber, A Pertusa, JMI Quereda
ISMIR, 61-66, 2007
CQT-based convolutional neural networks for audio scene classification
T Lidy, A Schindler
Proceedings of the detection and classification of acoustic scenes and …, 2016
On the suitability of state-of-the-art music information retrieval methods for analyzing, categorizing and accessing non-western and ethnic music collections
T Lidy, CN Silla Jr, O Cornelis, F Gouyon, A Rauber, CAA Kaestner, ...
Signal Processing 90 (4), 1032-1048, 2010
Automatic audio segmentation: Segment boundary and structure detection in popular music
E Peiszer, T Lidy, A Rauber
Proceedings of the International Workshop on Learning the Semantics of Audio …, 2008
DelosDLMS-the integrated DELOS digital library management system
M Agosti, S Berretti, G Brettlecker, A Del Bimbo, N Ferro, N Fuhr, D Keim, ...
International DELOS Conference, 36-45, 2007
Parallel convolutional neural networks for music genre and mood classification
T Lidy, A Schindler
MIREX2016, 2016
Combining audio and symbolic descriptors for music classification from audio
T Lidy, A Rauber, A Pertusa, J Inesta
Music Information Retrieval Information Exchange (MIREX), 2007
The map of mozart
R Mayer, T Lidy, A Rauber
na, 2006
A cartesian ensemble of feature subspace classifiers for music categorization
T Lidy, R Mayer, A Rauber, PJ Ponce de León Amador, A Pertusa, ...
International Society for Music Information Retrieval, 2010
Comparing Shallow versus Deep Neural Network Architectures for Automatic Music Genre Classification.
A Schindler, T Lidy, A Rauber
FMT, 17-21, 2016
Automatic audio segmentation: Segment boundary and structure detection in popular music
E Peiszer, T Lidy, A Rauber
A multi-modal deep neural network approach to bird-song identification
B Fazeka, A Schindler, T Lidy, A Rauber
arXiv preprint arXiv:1811.04448, 2018
Content-based organization of digital audio collections
R Neumayer, T Lidy, A Rauber
na, 2005
Method and system to organize and visualize media
T Lidy, W Jochum, E Peiszer
US Patent App. 13/808,484, 2013
Evaluation of new audio features and their utilization in novel music retrieval applications
T Lidy
na, 2006
Decision manifolds—a supervised learning algorithm based on self-organization
G Polzlbauer, T Lidy, A Rauber
IEEE Transactions on Neural Networks 19 (9), 1518-1530, 2008
Spectral convolutional neural network for music classification
T Lidy
Music information retrieval evaluation eX-change (MIREX), Malaga, Spain, 2015
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