Overview
- El Data Science for Music Challenge, a través del Michigan Institute for Data Science, aims to transform the music industry.
- They have launched four projects under this initiative
- These projects will use ML and DL techniques for the study of music theory and the connection between text and music..
Introduction
From digital beat enhancement to totally new song creation, machine learning is truly transforming the music industry. Many artists these days are using ML to boost their songs and add items to their albums that were previously unthinkable..
Researchers at the University of Michigan are also using machine learning to make their own mark on the digital age of music.. They are changing the way we understand, we create and interact with music.
Four research teams, using machine learning and deep learning tools and techniques for the study of music theory, interpretation, social media-based music creation and the connection between words and music, will receive expert support. They will be funded and these funds will be provided under the Data Science for Music Challenge initiative through the Michigan Institute for Data Science (MIDAS).
The main focus of these projects will be to use machine learning techniques to automate musical text accompaniment and data-driven analysis of musical performance.. Each project will receive $ 75,000 over a period of one year. Below are the projects:
Understanding and mining patterns of audience engagement and creative collaboration in collaborative large-scale musical performances
The researchers who are selected for this project will have the task of developing a platform for the creation and interpretation of collaborative music.. They should use data mining techniques to discover patterns in audience engagement.
Understand how the brain processes music through the sonatas of the Bach trio
This is probably the most fascinating project of all. Researchers will attempt to develop and analyze digitized performances of Bach's Trio Sonatas. They will be asked to produce algorithms that study the structure of music from a data science perspective. The ultimate goal is to understand what it is that makes performers so good artistically., as well as discovering the common mistakes they make.
The sound of the text
The goal of this project is to develop a data science framework that connects music with language. Researchers should develop tools that produce musical interpretations of texts, fully supported by emotion and content. As the name suggests, the ultimate goal is to create a tool that can transform any text into music.
A computational study of melodic structures modeled by means of musical cultures
This project aims to combine computational analysis and music theory. This will be done to compare music in six cultures, including indian songs, in order to identify commonalities about how music is generated and structured in different cultures.
You can read more about the MIDAS challenge here for more details.
Our opinion on this
This shows how far machine learning has penetrated the music industry and how far it has yet to go.. These projects are just the beginning, or the tip of the iceberg, who have the potential to start a revolution. Assuming these projects are successful, will broaden and deepen the current horizon in the world of digital music.
The results can also be applied to other interactive environments, including the development of new educational tools. What are the use cases you can think of for these projects? Let us know in the comment section!!
