Deep learning for audio signal processing H Purwins, B Li, T Virtanen, J Schlüter, SY Chang, T Sainath IEEE Journal of Selected Topics in Signal Processing 13 (2), 206-219, 2019 | 833 | 2019 |
Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification MS Treder, H Purwins, D Miklody, I Sturm, B Blankertz Journal of neural engineering 11 (2), 026009, 2014 | 92 | 2014 |
A new method for tracking modulations in tonal music in audio data format H Purwins, B Blankertz, K Obermayer Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural …, 2000 | 68 | 2000 |
Regression methods for virtual metrology of layer thickness in chemical vapor deposition H Purwins, B Barak, A Nagi, R Engel, U Höckele, A Kyek, S Cherla, ... IEEE/ASME Transactions on Mechatronics 19 (1), 1-8, 2013 | 61 | 2013 |
Profiles of pitch classes circularity of relative pitch and key-experiments, models, computational music analysis, and perspectives H Purwins PhD Diss. Technische Universität Berlin, 2005 | 58 | 2005 |
Computational models of music perception and cognition I: The perceptual and cognitive processing chain H Purwins, P Herrera, M Grachten, A Hazan, R Marxer, X Serra Physics of Life Reviews 5 (3), 151-168, 2008 | 48 | 2008 |
Sparse approximations for drum sound classification S Scholler, H Purwins IEEE Journal of Selected Topics in Signal Processing 5 (5), 933-940, 2011 | 45 | 2011 |
Computational models of music perception and cognition II: Domain-specific music processing H Purwins, M Grachten, P Herrera, A Hazan, R Marxer, X Serra Physics of Life Reviews 5 (3), 169-182, 2008 | 37 | 2008 |
Toroidal models in tonal theory and pitch-class analysis. H Purwins, B Blankertz, K Obermayer Computing in musicology 15, 2007 | 33* | 2007 |
Convolutional neural networks with batch normalization for classifying hi-hat, snare, and bass percussion sound samples N Gajhede, O Beck, H Purwins Proceedings of the Audio Mostly 2016, 111-115, 2016 | 32 | 2016 |
Unsupervised incremental online learning and prediction of musical audio signals R Marxer, H Purwins IEEE/ACM Transactions on Audio, Speech, and Language Processing 24 (5), 863-874, 2016 | 27* | 2016 |
Correspondence analysis for visualizing interplay of pitch class, key, and composer H Purwins, T Graepel, B Blankertz, K Obermayer Perspectives in mathematical and computational music theory, Osnabrück …, 2004 | 27 | 2004 |
Regression methods for prediction of PECVD Silicon Nitride layer thickness H Purwins, A Nagi, B Barak, U Höckele, A Kyek, B Lenz, G Pfeifer, ... 2011 IEEE International Conference on Automation Science and Engineering …, 2011 | 24 | 2011 |
Closing the loop of sound evaluation and design P Susini, N Misdariis, G Lemaitre, O Houix, D Rocchesso, P Polotti, ... Perceptual Quality of Systems 2 (4), 2006 | 24 | 2006 |
Computing auditory perception H Purwins, B Blankertz, K Obermayer Organised Sound 5 (3), 159-171, 2000 | 22 | 2000 |
Model cortical responses for the detection of perceptual onsets and beat tracking in singing M Coath, SL Denham, LM Smith, H Honing, A Hazan, P Holonowicz, ... Connection Science 21 (2-3), 193-205, 2009 | 21 | 2009 |
What/when causal expectation modelling applied to audio signals A Hazan, R Marxer, P Brossier, H Purwins, P Herrera, X Serra Connection Science 21 (2-3), 119-143, 2009 | 21 | 2009 |
Constant Q profiles for tracking modulations in audio data P Purwins, B Blankertz, K Obermayer International Computer Music Conference, 2001 | 20 | 2001 |
Dynamical hierarchical self-organization of harmonic, motivic, and pitch categories R Marxer, P Holonowicz, H Purwins, A Hazan Music, Brain and Cognition. Part 2, 2007 | 19 | 2007 |
A graphical representation and dissimilarity measure for basic everyday sound events K Adiloglu, A Annies, E Wahlen, H Purwins, K Obermayer IEEE Transactions on Audio, Speech, and Language Processing 20 (5), 1542-1552, 2012 | 18 | 2012 |