Explored the audiovisual storytelling of the show "Off the Air" using a variety of multimodal processing and machine learning tools. These include PCA clustering, K-means color extractions, frame-level audio classification, and real-time visualization of audio features.
Trained a machine learning model to extract posteriograms and word alignments from a song and then tested whether these features could be used to create more nuanced vocal source separation models. Described in research paper with code on Github.
Created a high-performance script that facilitates async calls within a multiprocessing framework to simulate an active music listener, allowing Spotify's API rate-limiting to be bypassed. This resulted in being able to extract almost all artists on Spotify (over 13 million) in a very small amount of time.
Designed an abstract mandala generator that constantly changes form using the current second, minute, hour, and day
Example songs made by my auto-remixing program which decomposes all songs in a music library into their stems using a source separation model. It then repeatedly chooses four stems at random that it warps to match in tempo, pitch, and onset position creating a cohesive output song.
Created a PostgreSQL database and a corresponding Flask web interface to store music library data dynamically with multiple linked versions of songs
Created dynamic visualizations of the nature and efficacy of the various methods to find all pairs and triples in an array that sum to a value. Created graphic comparing time and operations for each type of algorithm.
A simple but powerful tool to compare Covid-19 stats amongst the five boroughs of NYC relative to major events during this time.