| Music emotion recognition: A state of the art review YE Kim, EM Schmidt, R Migneco, BG Morton, P Richardson, J Scott, ... Proc. ismir 86, 937-952, 2010 | 700 | 2010 |
| 1000 songs for emotional analysis of music M Soleymani, MN Caro, EM Schmidt, CY Sha, YH Yang Proceedings of the 2nd ACM international workshop on Crowdsourcing for …, 2013 | 266 | 2013 |
| End-to-end learning for music audio tagging at scale J Pons, O Nieto, M Prockup, E Schmidt, A Ehmann, X Serra arXiv preprint arXiv:1711.02520, 2017 | 265 | 2017 |
| Moodswings: A collaborative game for music mood label collection. YE Kim, EM Schmidt, L Emelle Ismir 8, 231-236, 2008 | 164 | 2008 |
| Feature selection for content-based, time-varying musical emotion regression EM Schmidt, D Turnbull, YE Kim Proceedings of the international conference on Multimedia information …, 2010 | 142 | 2010 |
| Prediction of time-varying musical mood distributions using Kalman filtering EM Schmidt, YE Kim 2010 Ninth International Conference on Machine Learning and Applications …, 2010 | 130 | 2010 |
| Learning emotion-based acoustic features with deep belief networks EM Schmidt, YE Kim 2011 IEEE workshop on applications of signal processing to audio and …, 2011 | 116 | 2011 |
| Modeling Musical Emotion Dynamics with Conditional Random Fields. EM Schmidt, YE Kim ISMIR 11, 777-782, 2011 | 111 | 2011 |
| A Comparative Study of Collaborative vs. Traditional Musical Mood Annotation. JA Speck, EM Schmidt, BG Morton, YE Kim ISMIR 104, 549-554, 2011 | 95 | 2011 |
| Automatic multi-track mixing using linear dynamical systems J Scott, M Prockup, EM Schmidt, YE Kim Proceedings of the 8th Sound and Music Computing Conference, Padova, Italy 12, 2011 | 54 | 2011 |
| Feature Learning in Dynamic Environments: Modeling the Acoustic Structure of Musical Emotion. EM Schmidt, JJ Scott, YE Kim ISMIR, 325-330, 2012 | 49 | 2012 |
| Improving music emotion labeling using human computation BG Morton, JA Speck, EM Schmidt, YE Kim Proceedings of the acm sigkdd workshop on human computation, 45-48, 2010 | 39 | 2010 |
| Modeling Genre with the Music Genome Project: Comparing Human-Labeled Attributes and Audio Features. M Prockup, AF Ehmann, F Gouyon, EM Schmidt, O Celma, YE Kim ISMIR, 31-37, 2015 | 29 | 2015 |
| Learning Rhythm And Melody Features With Deep Belief Networks. EM Schmidt, YE Kim ISMIR, 21-26, 2013 | 26 | 2013 |
| The MediaEval 2013 Brave New Task: Emotion in Music. M Soleymani, MN Caro, EM Schmidt, YH Yang MediaEval, 2013 | 24 | 2013 |
| Utilizing music technology as a model for creativity development in K-12 education D Rosen, EM Schmidt, YE Kim Proceedings of the 9th ACM Conference on Creativity & Cognition, 341-344, 2013 | 23 | 2013 |
| Teaching stem concepts through music technology and dsp YE Kim, AM Batula, R Migneco, P Richardson, B Dolhansky, D Grunberg, ... 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP …, 2011 | 19 | 2011 |
| Modeling Musical Rhythm at Scale with the Music Genome Project M Prockup, AF Ehmann, F Gouyon, EM Schmidt, YE Kim Applications of Signal Processing to Audio and Acoustics (WASPAA), 2015 IEEE …, 2015 | 18 | 2015 |
| Mood classification using listening data F Korzeniowski, O Nieto, M McCallum, M Won, S Oramas, E Schmidt arXiv preprint arXiv:2010.11512, 2020 | 17 | 2020 |
| Efficient Acoustic Feature Extraction for Music Information Retrieval Using Programmable Gate Arrays. EM Schmidt, K West, YE Kim ISMIR, 273-278, 2009 | 15 | 2009 |
Youngmoo KimProfessor of Electrical and Computer Engineering, Drexel UniversityVerified email at drexel.edu