Erik M. Schmidt

According to our database, Erik M. Schmidt authored at least 25 papers between 2008 and 2020.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2020

Mood Classification Using Listening Data.

Filip Korzeniowski, Oriol Nieto, Matthew C. McCallum, Minz Won, Sergio Oramas, Erik M. Schmidt

Proceedings of the 21th International Society for Music Information Retrieval Conference, 2020

2018

End-to-end Learning for Music Audio Tagging at Scale.

Jordi Pons, Oriol Nieto, Matthew Prockup, Erik M. Schmidt, Andreas F. Ehmann, Xavier Serra

Proceedings of the 19th International Society for Music Information Retrieval Conference, 2018

2015

Modeling musical rhythmatscale with the music Genome project.

Matthew Prockup, Andreas F. Ehmann, Fabien Gouyon, Erik M. Schmidt, Youngmoo E. Kim

Proceedings of the 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2015

Modeling Genre with the Music Genome Project: Comparing Human-Labeled Attributes and Audio Features.

Matthew Prockup, Andreas F. Ehmann, Fabien Gouyon, Erik M. Schmidt, Òscar Celma, Youngmoo E. Kim

Proceedings of the 16th International Society for Music Information Retrieval Conference, 2015

2013

Demos and Late-Breaking Session of the Thirteenth International Society for Music Information Retrieval Conference (ISMIR 2012).

Amelie Anglade, Eric Humphrey, Erik M. Schmidt, Sebastian Stober, Mohamed Sordo

Comput. Music. J., 2013

1000 songs for emotional analysis of music.

Mohammad Soleymani, Michael N. Caro, Erik M. Schmidt, Cheng-Ya Sha, Yi-Hsuan Yang

Proceedings of the 2nd ACM international workshop on Crowdsourcing for multimedia, 2013

The MediaEval 2013 Brave New Task: Emotion in Music.

Mohammad Soleymani, Michael N. Caro, Erik M. Schmidt, Yi-Hsuan Yang

Proceedings of the MediaEval 2013 Multimedia Benchmark Workshop, 2013

Learning Rhythm And Melody Features With Deep Belief Networks.

Erik M. Schmidt, Youngmoo E. Kim

Proceedings of the 14th International Society for Music Information Retrieval Conference, 2013

Toward Understanding Expressive Percussion Through Content Based Analysis.

Matthew Prockup, Erik M. Schmidt, Jeffrey J. Scott, Youngmoo E. Kim

Proceedings of the 14th International Society for Music Information Retrieval Conference, 2013

Utilizing music technology as a model for creativity development in K-12 education.

David S. Rosen, Erik M. Schmidt, Youngmoo E. Kim

Proceedings of the Creativity and Cognition 2013, 2013

2012

Synthetic Emotions for Humanoids: Perceptual Effects of Size and Number of Robot Platforms.

David Grunberg, Alyssa M. Batula, Erik M. Schmidt, Youngmoo E. Kim

Int. J. Synth. Emot., 2012

Feature Learning in Dynamic Environments: Modeling the Acoustic Structure of Musical Emotion.

Erik M. Schmidt, Jeffrey J. Scott, Youngmoo E. Kim

Proceedings of the 13th International Society for Music Information Retrieval Conference, 2012

Affective gesturing with music mood recognition.

David Grunberg, Alyssa M. Batula, Erik M. Schmidt, Youngmoo E. Kim

Proceedings of the 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), Osaka, Japan, November 29, 2012

Analyzing the Perceptual Salience of Audio Features for Musical Emotion Recognition.

Erik M. Schmidt, Matthew Prockup, Jeffrey J. Scott, Brian Dolhansky, Brandon G. Morton, Youngmoo E. Kim

Proceedings of the From Sounds to Music and Emotions - 9th International Symposium, 2012

2011

Modeling musical instrument tones as dynamic textures.

Erik M. Schmidt, Raymond Migneco, Jeffrey J. Scott, Youngmoo E. Kim

Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2011

Learning emotion-based acoustic features with deep belief networks.

Erik M. Schmidt, Youngmoo E. Kim

Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 2011

A Comparative Study of Collaborative vs. Traditional Musical Mood Annotation.

Jacquelin A. Speck, Erik M. Schmidt, Brandon G. Morton, Youngmoo E. Kim

Proceedings of the 12th International Society for Music Information Retrieval Conference, 2011

Modeling Musical Emotion Dynamics with Conditional Random Fields.

Erik M. Schmidt, Youngmoo E. Kim

Proceedings of the 12th International Society for Music Information Retrieval Conference, 2011

2010

Feature selection for content-based, time-varying musical emotion regression.

Erik M. Schmidt, Douglas Turnbull, Youngmoo E. Kim

Proceedings of the 11th ACM SIGMM International Conference on Multimedia Information Retrieval, 2010

Prediction of Time-varying Musical Mood Distributions from Audio.

Erik M. Schmidt, Youngmoo E. Kim

Proceedings of the 11th International Society for Music Information Retrieval Conference, 2010

State of the Art Report: Music Emotion Recognition: A State of the Art Review.

Youngmoo E. Kim, Erik M. Schmidt, Raymond Migneco, Brandon G. Morton, Patrick Richardson, Jeffrey J. Scott, Jacquelin A. Speck, Douglas Turnbull

Proceedings of the 11th International Society for Music Information Retrieval Conference, 2010

Prediction of Time-Varying Musical Mood Distributions Using Kalman Filtering.

Erik M. Schmidt, Youngmoo E. Kim

Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010

Improving music emotion labeling using human computation.

Brandon G. Morton, Jacquelin A. Speck, Erik M. Schmidt, Youngmoo E. Kim

Proceedings of the ACM SIGKDD Workshop on Human Computation, 2010

2009

Efficient Acoustic Feature Extraction for Music Information Retrieval Using Programmable Gate Arrays.

Erik M. Schmidt, Kris West, Youngmoo E. Kim

Proceedings of the 10th International Society for Music Information Retrieval Conference, 2009

2008

MoodSwings: A Collaborative Game for Music Mood Label Collection.

Youngmoo E. Kim, Erik M. Schmidt, Lloyd Emelle

Proceedings of the ISMIR 2008, 2008