I am a PhD student working in Dr. Youngmoo Kim's lab. I graduated from the University of Maryland, Baltimore County in 2009. My research focuses on influence recognition between musicians. In the past, composers would often pay homage to those that came before them by borrowing musical phrases or concepts. During the era of recorded music, the influence of older artists on younger musicians has continued at an even quicker pace.  For example, James Brown, who was popular in the 1950's and 60's influenced Michael Jackson (popular in the 70's and 80's). Michael Jackson went on to influence Chris Brown (popular in the 2000's and 2010's). By examining the flow of influence, we can obtain an interesting look into how music has evolved and adapted throughout history. Musicologists have been interested in the topic of influence between composers for centuries and have developed methods and heuristics for determining influence in classical music. Their methods usually involve looking for similarities in the musical score and while this works well for music where the score is the primary (canonical) source of information, this type of analysis is not well suited for modern popular music, where the audio recording itself is arguably the primary representation. For my research, I am attempting to learn a mapping between information extracted from the audio data and influence relationship data to learn a useful feature recognizing influence.

Additionally, I have been part of the National Science Foundation (NSF) GK-12 Fellowship for three years in conjunction with working towards a doctorate in Electrical Engineering at Drexel University. The GK-12 Fellowship encourages K-12 students to become interested in the STEM fields by embedding graduate students directly in the classroom. As one of these graduate students, I was responsible for developing projects and lessons that incorporate their area of research area along with the National Academy of Engineering Grand Challenges. 


  • Musical Influence Recognition
  • MoodSwings


  • M.S. Electrical Engineering - Drexel University - 2011
  • B.S. Computer Engineering - University of Maryland, Baltimore County - 2009

Music Information Retrieval Publications

  • B. G. Morton and Y. E. Kim, “Acoustic Features For Recognizing Musical Artist Influence,” in International Conference on Machine Learning and Applications (ICMLA 2015), 2015.

  • E. M. Schmidt, M. Prockup, J. Scott, B. Dolhansky, B. G. Morton, and Y. E. Kim, “Relating Perceptual and Feature Space Invariances in Music Emotion Recognition,” in 9th International Symposium on Computer Music Modeling and Retrieval, 2012, no. June, pp. 534–542.

  • J. Scott, E. M. Schmidt, M. Prockup, B. Morton, and Y. E. Kim, “Predicting Time-Varying Musical Emotion Distributions from Multi-Track Audio,” in 9th International Symposium on Computer Music Modelling and Retrieval (CMMR 2012), 2012, no. June, pp. 186–193.

  • J. A. Speck, E. M. Schmidt, B. G. Morton, and Y. E. Kim, “A Comparative Study of Collaborative vs. Traditional Musical Mood Annotation,” in 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 2011, no. Ismir, pp. 549–554.

  • J. Scott, R. Migneco, B. Morton, C. M. Hahn, P. Diefenbach, and Y. E. Kim, “An Audio Processing Library For MIR Application Development In Flash,” in 11th International Society for Music Information Retrieval Conference (ISMIR 2010), 2010, no. ISMIR, pp. 643–648.

  • Y. E. Kim, E. M. Schmidt, R. Migneco, B. G. Morton, P. Richardson, J. Scott, J. A. Speck, and D. Turnbull, “Music Emotion Recognition : A State Of The Art Review,” in 11th International Society for Music Information Retrieval ConferenceI (ISMIR 2010), 2010, no. ISMIR, pp. 255–266.

  • B. G. Morton, J. a. Speck, E. M. Schmidt, and Y. E. Kim, “Improving music emotion labeling using human computation,” ACM SIGKDD Work. Hum. Comput. (HCOMP 2010), pp. 45–48, 2010.


  • B. G. Morton, Y. E. Kim, M. N. VanKouwenberg, C. Lehmann, and J. S. Ward, “Developing Curriculum For Introducing CyberSecurity To K-12 Students,” in ASEE Annual Conference (ASEE 2014), 2014.

  • J. Gregorio, B. G. Morton, and Y. E. Kim, “Music Technology as a Vehicle to STEM / STEAM for High School Students,” in ASEE Annual Conference, 2013.

  • A. M. Batula, B. G. Morton, R. Migneco, M. Prockup, E. M. Schmidt, D. K. Grunberg, Y. E. Kim, and A. K. Fontecchio, “Music Technology as an Introduction to STEM,” in ASEE Annual Conference (ASEE 2012), 2012.

  • Y. E. Kim, A. M. Batula, R. Migneco, P. Richardson, B. Dolhansky, D. Grunberg, B. Morton, M. Prockup, E. M. Schmidt, and J. Scott, “Teaching STEM Concepts Through Music Technology and DSP,” in 14th IEEE Digital Signal Processing Workshop and 6th IEEE Signal Processing Education Workshop, 2011, pp. 220–225.