Evaluation of feature extractors and psycho-acoustic transformations for music genre classification T Lidy, A Rauber ISMIR, 34-41	291	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.	156	2002
Experimenting with musically motivated convolutional neural networks J Pons, T Lidy, X Serra 2016 14th international workshop on content-based multimedia indexing (CBMI …	114	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	108	2007
CQT-based convolutional neural networks for audio scene classification T Lidy, A Schindler Proceedings of the detection and classification of acoustic scenes and …	93	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	71	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 …	42	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	34	2007
Parallel convolutional neural networks for music genre and mood classification T Lidy, A Schindler MIREX2016	32	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)	30	2007
The map of mozart R Mayer, T Lidy, A Rauber na	24	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	23	2010
Comparing Shallow versus Deep Neural Network Architectures for Automatic Music Genre Classification. A Schindler, T Lidy, A Rauber FMT, 17-21	22	2016
Automatic audio segmentation; segment boundary and structure detection in popular music E Peiszer	22	2007
A multi-modal deep neural network approach to bird-song identification B Fazeka, A Schindler, T Lidy, A Rauber arXiv preprint arXiv:1811.04448	21	2018
Content-based organization of digital audio collections R Neumayer, T Lidy, A Rauber na	21	2005
Method and system to organize and visualize media T Lidy, W Jochum, E Peiszer US Patent App. 13/808,484	18	2013
Evaluation of new audio features and their utilization in novel music retrieval applications T Lidy na	16	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	15	2008
Spectral convolutional neural network for music classification T Lidy Music information retrieval evaluation eX-change (MIREX), Malaga, Spain	14	2015
Music information technology and professional stakeholder audiences: Mind the adoption gap C Liem, A Rauber, T Lidy, R Lewis, C Raphael, JD Reiss, T Crawford, ... Dagstuhl follow-ups 3	13	2012
Sound Re-synthesis from rhythm Pattern Features-audible insight into a Music Feature Extraction Process. T Lidy, G Pölzlbauer, A Rauber Ann Arbor, MI: Michigan Publishing, University of Michigan Library	13	2005
Combined fluctuation features for music genre classification T Lidy, A Rauber Music Information Retrieval Evaluation eXchange	13	2005
Ambient music experience in real and virtual worlds using audio similarity J Frank, T Lidy, E Peiszer, R Genswaider, A Rauber Proceedings of the 1st ACM international workshop on Semantic ambient media …	12	2008
Multi-temporal resolution convolutional neural networks for acoustic scene classification A Schindler, T Lidy, A Rauber arXiv preprint arXiv:1811.04419	11	2018
Diego Milano, Moira Norrie, Paola Ranaldi, Andreas Rauber, Hans-Jörg Schek, Tobias Schreck, Heiko Schuldt, Beat Signer, Michael Springmann, DelosDLMS-the integrated DELOS … M Agosti, S Berretti, G Brettlecker, A del Bimbo, N Ferro, N Fuhr, D Keim, ... Proceedings of the 1st international conference on Digital libraries …	11	2007
Mirex audio genre classification K West Music Information Retrival Evaluation eXchange (MIREX)	11	2005
Map-based music interfaces for mobile devices J Frank, T Lidy, P Hlavac, A Rauber Proceedings of the 16th ACM international conference on Multimedia, 981-982	10	2008
Classification and clustering of music for novel music access applications T Lidy, A Rauber Machine Learning Techniques for Multimedia, 249-285	9	2008
Visually profiling radio stations T Lidy, A Rauber ISMIR, 186-191	9	2006
Testing supervised classifiers based on non-negative matrix factorization to musical instrument classification E Benetos, C Kotropoulos, T Lidy, A Rauber 2006 14th European Signal Processing Conference, 1-5	8	2006
Genre-oriented organization of music collections using the somejb system: An analysis of rhythm patterns and other features T Lidy, A Rauber na	8	2003
Fashion and apparel classification using convolutional neural networks A Schindler, T Lidy, S Karner, M Hecker arXiv preprint arXiv:1811.04374	6	2018
Audio music classification using a combination of spectral, timbral, rhythmic, temporal and symbolic features T Lidy, A Rauber, A Pertusa, PJ Ponce de León Amador, JM Iñesta	6	2008
Bringing Mobile Map-Based Access to Digital Audio to the End User R Neumayer, J Frank, P Hlavac, T Lidy, A Rauber 14th International Conference of Image Analysis and Processing-Workshops …	6	2007
Mirex 2007 combining audio and symbolic descriptors for music classification from audio T Lidy, A Rauber, A Pertusa, JM Inesta MIREX 2007-Music Information Retrieval Evaluation eXchange	6	2007
Mirex 2005: Combined fluctuation features for music genre classification T Lidy, A Rauber Proceedings of the 6th Annual International Symposium on Music Information …	4	2005
Creating ambient music spaces in real and virtual worlds J Frank, T Lidy, E Peiszer, R Genswaider, A Rauber Multimedia Tools and Applications 44 (3), 449-468	3	2009
Music Information Retrieval T Lidy, A Rauber Handbook of Research on Digital Libraries: Design, Development, and Impact …	3	2009
Computing statistical spectrum descriptors for audio music similarity and retrieval T Lidy Third Music Information Retrieval Evaluation eXchange (MIREX), 2006	3	2006

6. Musimap’s Emotional AI: Decoding the DNA of Music by combining human emotions and AI to gain insight into music consumption. In conversation with Thomas Lidy, Chief Innovation Officer Musimap.

6. Musimap’s Emotional AI: Decoding the DNA of Music by combining human emotions and AI to gain insight into music consumption. In conversation with Thomas Lidy, Chief Innovation Officer Musimap.

Episode 6: In conversation with Thomas Lidy, Chief Innovation Officer Musimap.

Musimap's Emotional AI:

Decoding the DNA of music by combining human emotions and AI to gain insight into music consumption.

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Full version vlog episode

Musimap | Emotional AI | DNA Music | Music Consumption | FuturePulse

Thomas Lidy – one of the world’s top experts in AI Music revolutionizing the music-tech industry.

How FuturePulse is empowering the music industry with Predictive Analytics.

Semantic audio analysis and music recognition explained.

Bridging the Gap Between Academia and Industry: From Vienna University of Technology – Music Information Retrieval Research – Spectralmind – #Music Bricks – To Musimap.

The Belgian B2B music AI company Musimap is backed by 20 years of human research combined with audio-processing and AI.

Becoming the “Ultimate Music Assistant” by scientifically decoding the DNA of music.

A“humanized” algorithm = a human fused AI + feedback loop.

Is a Universal Music taxonomy possible?

MusiMe: You are what you listen to. Your Spotify playlists reveal your personality.

MusiMe: You are what you listen to​

Quincy Jones and Musimap to refashion music consumption with the psychographic profiling engine of MusiMe …

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Research by Thomas Lidy

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MusiMe: You are what you listen to