| A review of deep learning based methods for acoustic scene classification J Abeßer Applied Sciences 10 (6), 2020, 2020 | 83 | 2020 |
| Automatic Tablature Transcription of Electric Guitar Recordings by Estimation of Score-and Instrument-Related Parameters. C Kehling, J Abeßer, C Dittmar, G Schuller DAFx, 219-226, 2014 | 60 | 2014 |
| Music information retrieval meets music education C Dittmar, E Cano, J Abeßer, S Grollmisch Dagstuhl Follow-Ups 3, 2012 | 59 | 2012 |
| Feature-based extraction of plucking and expression styles of the electric bass guitar J Abeßer, H Lukashevich, G Schuller 2010 IEEE International Conference on Acoustics, Speech and Signal …, 2010 | 46 | 2010 |
| Inside the jazzomat: new perspectives for jazz research M Pfleiderer, K Frieler, J Abesser, WG Zaddach, B Burkhart Schott Music GmbH, 2017 | 44 | 2017 |
| Automatic detection of audio effects in guitar and bass recordings M Stein, J Abeßer, C Dittmar, G Schuller Audio Engineering Society Convention 128, 2010 | 43 | 2010 |
| New sonorities for jazz recordings: Separation and mixing using deep neural networks SI Mimilakis, E Cano, J Abeßer, G Schuller 2nd AES Workshop on Intelligent Music Production 13, 2016 | 31 | 2016 |
| From Multi-Labeling to Multi-Domain-Labeling: A Novel Two-Dimensional Approach to Music Genre Classification. HM Lukashevich, J Abeßer, C Dittmar, H Grossmann ISMIR, 459-464, 2009 | 31 | 2009 |
| Jazz Solo Instrument Classification with Convolutional Neural Networks, Source Separation, and Transfer Learning. JS Gómez, J Abeßer, E Cano ISMIR, 577-584, 2018 | 30 | 2018 |
| Sounding industry: Challenges and datasets for industrial sound analysis S Grollmisch, J Abeßer, J Liebetrau, H Lukashevich 2019 27th European Signal Processing Conference (EUSIPCO), 1-5, 2019 | 29 | 2019 |
| Midlevel analysis of monophonic jazz solos: A new approach to the study of improvisation K Frieler, M Pfleiderer, WG Zaddach, J Abeßer Musicae Scientiae 20 (2), 143-162, 2016 | 28 | 2016 |
| Automatic quality assessment of vocal and instrumental performances of ninth-grade and tenth-grade pupils J Abeßer, J Hasselhorn, C Dittmar, A Lehmann, S Grollmisch Proceedings of the International Symposium on Computer Music …, 2013 | 28 | 2013 |
| Automatic string detection for bass guitar and electric guitar J Abeßer International Symposium on Computer Music Modeling and Retrieval, 333-352, 2012 | 27 | 2012 |
| Acoustic Scene Classification by Combining Autoencoder-Based Dimensionality Reduction and Convolutional Neural Networks. J Abeßer, SI Mimilakis, R Gräfe, HM Lukashevich, I Fraunhofer DCASE, 7-11, 2017 | 24 | 2017 |
| Automatic genre classification of latin american music using characteristic rhythmic patterns T Völkel, J Abeßer, C Dittmar, H Großmann Proceedings of the 5th Audio Mostly Conference: A Conference on Interaction …, 2010 | 24 | 2010 |
| Data-driven solo voice enhancement for jazz music retrieval S Balke, C Dittmar, J Abeßer, M Müller 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 22 | 2017 |
| Automatic classification of musical pieces into global cultural areas A Kruspe, H Lukashevich, J Abeßer, H Großmann, C Dittmar Audio Engineering Society Conference: 42nd International Conference …, 2011 | 21 | 2011 |
| Inside the Jazzomat M Pfleiderer, K Frieler, J Abeßer, WG Zaddach, B Burkhart New Perspectives for Jazz Research, 2017 | 18 | 2017 |
| Score-informed analysis of tuning, intonation, pitch modulation, and dynamics in jazz solos J Abeßer, K Frieler, E Cano, M Pfleiderer, WG Zaddach IEEE/ACM Transactions on Audio, Speech, and Language Processing 25 (1), 168-177, 2016 | 18 | 2016 |
| Towards Evaluating Multiple Predominant Melody Annotations in Jazz Recordings. S Balke, J Driedger, J Abeßer, C Dittmar, M Müller ISMIR, 246-252, 2016 | 18 | 2016 |
