8 citations · 8 across the 3 of their papers we have counts for
5 papers
JazzSAMBA: A Synchronous and Asynchronous Multi-take Band Audio Dataset of Jazz Standards for Live Music Models
Phillip Long, Jacob Nguyen, Jace Hosto +9
Machine learning has made strong progress on music tasks, both as assistive tools and as creative partners. However, most systems train on multitrack corpora that emphasize pop and…
MusPyExpress: Extending MusPy with Enhanced Expression Text Support
Phillip Long, Hao-Wen Dong, Julian McAuley +1
Current work in modeling symbolic music primarily relies on representations extracted from MIDI-like data. While such formats allow for modeling symbolic music as sequences of note…
Benchmarking Language Modeling for Lossless Compression of Full-Fidelity Audio
Phillip Long, Zachary Novack, Chris Donahue
Autoregressive "language" models (LMs) trained on raw waveforms can be repurposed for lossless audio compression, but prior work is limited to 8-bit audio, leaving open whether suc…
MuseTok: Symbolic Music Tokenization for Generation and Semantic Understanding
Jingyue Huang, Zachary Novack, Phillip Long +4
Discrete representation learning has shown promising results across various domains, including generation and understanding in image, speech and language. Inspired by these advance…
PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing
Phillip Long, Zachary Novack, Taylor Berg-Kirkpatrick +1
The recent explosion of generative AI-Music systems has raised numerous concerns over data copyright, licensing music from musicians, and the conflict between open-source AI and la…