Fundamentals of Music Processing

See a recent syllabus.

We begin by introducing frequency analysis using the Discrete Fourier Transform, and its commonly used relative, the Short-Time Fourier Transform.  From there we study features extraction methods such as chroma analysis and onset detection, and examine analysis tools like dynamic-time-warping and self-similarity matrices. These techniques serve to develop a variety of music analysis applications, including:

• • Onset classification

• • Temporal alignment of different renditions of the same music

• • Automatic chord recognition and key detection

• • Tempo and beat tracking

• • Structural analysis of music

• • Content-based audio retrieval

• • Music-based audio decomposition

Students practice these techniques with in-class lab exercises and coding assignments in python. Students taking the graduate version (21M.587) complete an independent final project as well.