240 citations · 323 across the 7 of their papers we have counts for
11 papers
Melody transcription via generative pre-training
Chris Donahue, John Thickstun, Percy Liang
Despite the central role that melody plays in music perception, it remains an open challenge in music information retrieval to reliably detect the notes of the melody present in an…
It's Raw! Audio Generation with State-Space Models
Karan Goel, Albert Gu, Chris Donahue +1
Developing architectures suitable for modeling raw audio is a challenging problem due to the high sampling rates of audio waveforms. Standard sequence modeling approaches like RNNs…
Codified audio language modeling learns useful representations for music information retrieval
Rodrigo Castellon, Chris Donahue, Percy Liang
We demonstrate that language models pre-trained on codified (discretely-encoded) music audio learn representations that are useful for downstream MIR tasks. Specifically, we explor…
Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music
Hao-Wen Dong, Chris Donahue, Taylor Berg-Kirkpatrick +1
Modern keyboards allow a musician to play multiple instruments at the same time by assigning zones -- fixed pitch ranges of the keyboard -- to different instruments. In this paper,…
Enabling Language Models to Fill in the Blanks
Chris Donahue, Mina Lee, Percy Liang
We present a simple approach for text infilling, the task of predicting missing spans of text at any position in a document. While infilling could enable rich functionality especia…
LakhNES: Improving multi-instrumental music generation with cross-domain pre-training
Chris Donahue, Huanru Henry Mao, Yiting Ethan Li +2
We are interested in the task of generating multi-instrumental music scores. The Transformer architecture has recently shown great promise for the task of piano score generation; h…