7 citations · 19 across the 13 of their papers we have counts for
13 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…
Fast Timing-Conditioned Latent Audio Diffusion
Zach Evans, CJ Carr, Josiah Taylor +2
Generating long-form 44.1kHz stereo audio from text prompts can be computationally demanding. Further, most previous works do not tackle that music and sound effects naturally vary…
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…