13 citations · 35 across the 14 of their papers we have counts for
14 papers
SAME: A Semantically-Aligned Music Autoencoder
Julian D. Parker, Zach Evans, CJ Carr +4
Latent representations are at the heart of the majority of modern generative models. In the audio domain they are typically produced by a neural-audio-codec autoencoder. In this wo…
Stable Audio 3
Zach Evans, Julian D. Parker, Matthew Rice +4
Stable Audio 3 is a family of fast latent diffusion models (small, medium, large) for variable-length audio generation and editing. Since our models can generate several minutes of…
Low-Resource Guidance for Controllable Latent Audio Diffusion
Zachary Novack, Zack Zukowski, CJ Carr +6
Generative audio requires fine-grained controllable outputs, yet most existing methods require model retraining on specific controls or inference-time controls (\textit{e.g.}, guid…
Music and Artificial Intelligence: Artistic Trends
Jordi Pons, Zack Zukowski, Julian D. Parker +3
We study how musicians use artificial intelligence (AI) across formats like singles, albums, performances, installations, voices, ballets, operas, or soundtracks. We collect 337 mu…
Fast Text-to-Audio Generation with Adversarial Post-Training
Zachary Novack, Zach Evans, Zack Zukowski +8
Text-to-audio systems, while increasingly performant, are slow at inference time, thus making their latency unpractical for many creative applications. We present Adversarial Relat…
Scaling Transformers for Low-Bitrate High-Quality Speech Coding
Julian D Parker, Anton Smirnov, Jordi Pons +4
The tokenization of speech with neural audio codec models is a vital part of modern AI pipelines for the generation or understanding of speech, alone or in a multimodal context. Tr…
Stable Audio Open
Zach Evans, Julian D. Parker, CJ Carr +3
Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio mod…
Long-form music generation with latent diffusion
Zach Evans, Julian D. Parker, CJ Carr +3
Audio-based generative models for music have seen great strides recently, but so far have not managed to produce full-length music tracks with coherent musical structure from text…
ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production
Jackson Loth, Pedro Sarmento, CJ Carr +2
Recent work in the field of symbolic music generation has shown value in using a tokenization based on the GuitarPro format, a symbolic representation supporting guitar expressive…
ShredGP: Guitarist Style-Conditioned Tablature Generation
Pedro Sarmento, Adarsh Kumar, Dekun Xie +3
GuitarPro format tablatures are a type of digital music notation that encapsulates information about guitar playing techniques and fingerings. We introduce ShredGP, a GuitarPro tab…
GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers
Pedro Sarmento, Adarsh Kumar, Yu-Hua Chen +3
Recently, symbolic music generation with deep learning techniques has witnessed steady improvements. Most works on this topic focus on MIDI representations, but less attention has…
DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models
Pedro Sarmento, Adarsh Kumar, CJ Carr +3
Originating in the Renaissance and burgeoning in the digital era, tablatures are a commonly used music notation system which provides explicit representations of instrument fingeri…
Generating Black Metal and Math Rock: Beyond Bach, Beethoven, and Beatles
Zack Zukowski, CJ Carr
We use a modified SampleRNN architecture to generate music in modern genres such as black metal and math rock. Unlike MIDI and symbolic models, SampleRNN generates raw audio in the…
Generating Albums with SampleRNN to Imitate Metal, Rock, and Punk Bands
CJ Carr, Zack Zukowski
This early example of neural synthesis is a proof-of-concept for how machine learning can drive new types of music software. Creating music can be as simple as specifying a set of…