Put AI to Work
April 15-18, 2019
New York, NY
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Make music composing easier for amateurs: A hybrid machine learning approach

Baohong Sun (Cheung Kong Graduate School of Business)
4:55pm5:35pm Thursday, April 18, 2019
Interacting with AI
Location: Regent Parlor
Secondary topics:  AI case studies, Models and Methods

Who is this presentation for?

  • Entrepreneurs and those interested in computer-aided music making



Prerequisite knowledge

  • Familiarity with music theory (useful but not required)

What you'll learn

  • Explore a framework that unifies hidden Markov models and deep learn algorithms (RNN) with modeling components that consider long-term memory and semantics of music


Creating your own musical pieces is one of the most wonderful ways of enjoying music, but many lack the basic musical skills to do so. Andrew Caosun discusses a neural hidden Markov model (NHMM)—a hybrid of a hidden Markov process and a convolutional neural network algorithm with LSTM. This model takes users’ original musical ideas, automatically modifies the input, and generates musically appropriate melodies as output.

The model is extended to allow users to specify magnitude of revision, duration of music segment to be revised, choice of music genres, popularity of songs, and cocreation of songs in social settings. These extensions enhance user understanding of music knowledge, enrich their experience of self-music learning, and enable social aspects of music making. The model is trained using Columbia’s publicly available Million Songs Dataset.

Andrew also explains how he and his team designed a mobile application with an intuitive, interactive, and graphical user interface that’s suitable for the elderly and young children. Unlike most existing literature focusing on computer music composing itself, their research and application aim to use computers to aid human composition and enrich music education for those without musical training.

Photo of Baohong Sun

Baohong Sun

Cheung Kong Graduate School of Business

Baohong Sun is the Dean’s Distinguished Chair Professor of Marketing at Cheung Kong Graduate School of Business. Before joining CKGSB, she was the Carnegie Bosch Chair Professor of Marketing at Carnegie Mellon University. She holds a Ph.D. in economics from University of Southern California.
She develops dynamic structural models to investigate consumer response to cross-selling campaigns, loyalty programs, service allocation in service centers, new service channels, optimal design of subscription pricing, dynamic and proactive customer information management. Her recent research interest focuses on modeling dynamic and inter-dependent consumer decisions on e-commerce and social media platforms.
Her papers have been nominated for John Little Best Paper Award and Long-Term Impact Award by Informs and won CART Research Frontier Award for Innovative Research at CMU. She won All Star Teaching Awards and was selected Master of MBA teaching. She also won George Leland Bach Teaching Award at CMU. She serves on the editorial boards of Journal of Marketing Research, Marketing Science, Journal of Marketing etc.

Her work and speech have been cited by medias such as the Economist, Wall Street Journal, New York Times, Bloomberg and BBC. She speaks at global conferences such as Summer and Winter Davos.