“‘AI Music’ Tag”,2019-09-28 (Jukebox, music)⁠:

Bibliography for tag ai/music, most recent first: 1 related tag, 88 annotations, & 160 links (parent).

• See Also
• Gwern
  • “GPT-2 Preference Learning for Music Generation”, Gwern2019
• Links
  • “GSoC 2024: Differentiable Logic for Interactive Systems and Generative Music”
  • “A.L.S. Stole His Voice. A.I. Retrieved It.”
  • “SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound”, Liu et al 2024
  • “Long-Form Music Generation With Latent Diffusion”, Evans et al 2024
  • “An Accurate and Rapidly Calibrating Speech Neuroprosthesis”, Card et al 2024
  • “OpenVoice: Versatile Instant Voice Cloning”, Qin et al 2023
  • “A Disney Director Tried—And Failed—To Use an AI Hans Zimmer to Create a Soundtrack”, Heikkilä2023
  • “Whisper-AT: Noise-Robust Automatic Speech Recognizers Are Also Strong General Audio Event Taggers”, Gong et al 2023
  • “High-Fidelity Audio Compression With Improved RVQGAN”, Kumar et al 2023
  • “Vocos: Closing the Gap between Time-Domain and Fourier-Based Neural Vocoders for High-Quality Audio Synthesis”, Siuzdak2023
  • “Voice Conversion With Just Nearest Neighbors”, Baas et al 2023
  • “SoundStorm: Efficient Parallel Audio Generation”, Borsos et al 2023
  • “ImageBind: One Embedding Space To Bind Them All”, Girdhar et al 2023
  • “TANGO: Text-To-Audio Generation Using Instruction-Tuned LLM and Latent Diffusion Model”, Ghosal et al 2023
  • “CLaMP: Contrastive Language-Music Pre-Training for Cross-Modal Symbolic Music Information Retrieval”, Wu et al 2023
  • “Speak, Read and Prompt (SPEAR-TTS): High-Fidelity Text-To-Speech With Minimal Supervision”, Kharitonov et al 2023
  • “Msanii: High Fidelity Music Synthesis on a Shoestring Budget”, Maina2023
  • “Archisound: Audio Generation With Diffusion”, Schneider2023
  • “Rock Guitar Tablature Generation via Natural Language Processing”, Casco-Rodriguez2023
  • “VALL-E: Neural Codec Language Models Are Zero-Shot Text to Speech Synthesizers”, Wang et al 2023
  • “High Fidelity Neural Audio Compression”, Défossez et al 2022
  • “Hierarchical Diffusion Models for Singing Voice Neural Vocoder”, Takahashi et al 2022
  • “RealSinger: Ultra-Realistic Singing Voice Generation via Stochastic Differential Equations”, Anonymous2022
  • “AudioLM: a Language Modeling Approach to Audio Generation”, Borsos et al 2022
  • “MeloForm: Generating Melody With Musical Form Based on Expert Systems and Neural Networks”, Lu et al 2022
  • “AI Composer Bias: Listeners like Music Less When They Think It Was Composed by an AI”, Shank et al 2022
  • “Musika! Fast Infinite Waveform Music Generation”, Pasini & Schlüter2022
  • “Multitrack Music Transformer: Learning Long-Term Dependencies in Music With Diverse Instruments”, Dong et al 2022
  • “BigVGAN: A Universal Neural Vocoder With Large-Scale Training”, Lee et al 2022
  • “CLAP: Learning Audio Concepts From Natural Language Supervision”, Elizalde et al 2022
  • “Tradformer: A Transformer Model of Traditional Music Transcriptions”, Casini & Sturm2022
  • “SymphonyNet: Symphony Generation With Permutation Invariant Language Model”, Liu et al 2022
  • “It’s Raw! Audio Generation With State-Space Models”, Goel et al 2022
  • “General-Purpose, Long-Context Autoregressive Modeling With Perceiver AR”, Hawthorne et al 2022
  • “FIGARO: Generating Symbolic Music With Fine-Grained Artistic Control”, Rütte et al 2022
  • “Steerable Discovery of Neural Audio Effects”, Steinmetz & Reiss2021
  • “Semi-Supervised Music Tagging Transformer”, Won et al 2021
  • “AudioCLIP: Extending CLIP to Image, Text and Audio”, Guzhov et al 2021
  • “MLP Singer: Towards Rapid Parallel Korean Singing Voice Synthesis”, Tae et al 2021
  • “PriorGrad: Improving Conditional Denoising Diffusion Models With Data-Dependent Adaptive Prior”, Lee et al 2021
  • “DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism”, Liu et al 2021
  • “Symbolic Music Generation With Diffusion Models”, Mittal et al 2021
  • “Interacting With GPT-2 to Generate Controlled and Believable Musical Sequences in ABC Notation”, Geerlings & Meroño-Peñuela2020
  • “AI Song Contest: Human-AI Co-Creation in Songwriting”, Huang et al 2020
  • “HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis”, Kong et al 2020
  • “DeepSinger: Singing Voice Synthesis With Data Mined From the Web”, Ren et al 2020
  • “Learning Music Helps You Read: Using Transfer to Study Linguistic Structure in Language Models”, Papadimitriou & Jurafsky2020
  • “Pony Voice Event—What People Forced Ponies to Say!”, Daily 2020
  • “15.ai”, Fifteen-kun & Project2020
  • “Pop Music Transformer: Beat-Based Modeling and Generation of Expressive Pop Piano Compositions”, Huang & Yang2020
  • “Writing the Next American Hit: Using GPT-2 to Explore the Possibility of Creating Successful AI-Generated Song Lyrics Possibility of Creating Successful AI-Generated Song Lyric”, Barrio2020
  • “Encoding Musical Style With Transformer Autoencoders”, Choi et al 2019
  • “GPT-2 Folk Music”, Gwern & Presser2019
  • “Parallel WaveGAN: A Fast Waveform Generation Model Based on Generative Adversarial Networks With Multi-Resolution Spectrogram”, Yamamoto et al 2019
  • “Low-Dimensional Embodied Semantics for Music and Language”, Raposo et al 2019
  • “MuseNet: a Deep Neural Network That Can Generate 4-Minute Musical Compositions With 10 Different Instruments, and Can Combine Styles from Country to Mozart to the Beatles”, Payne2019
  • “Generative Modeling With Sparse Transformers: We’ve Developed the Sparse Transformer, a Deep Neural Network Which Sets New Records at Predicting What Comes next in a Sequence—Whether Text, Images, or Sound. It Uses an Algorithmic Improvement of the attention Mechanism to Extract Patterns from Sequences 30× Longer Than Possible Previously”, Child & Gray2019
  • “Music Transformer: Generating Music With Long-Term Structure”, Huang et al 2018
  • “FloWaveNet: A Generative Flow for Raw Audio”, Kim et al 2018
  • “Piano Genie”, Donahue et al 2018
  • “Music Transformer”, Huang et al 2018
  • “This Time With Feeling: Learning Expressive Musical Performance”, Oore et al 2018
  • “The Challenge of Realistic Music Generation: Modeling Raw Audio at Scale”, Dieleman et al 2018
  • “The Sound of Pixels”, Zhao et al 2018
  • “Efficient Neural Audio Synthesis”, Kalchbrenner et al 2018
  • “Generating Structured Music through Self-Attention”, Huang et al 2018
  • “Towards Deep Modeling of Music Semantics Using EEG Regularizers”, Raposo et al 2017
  • “Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models”, Guimaraes et al 2017
  • “Neural Audio Synthesis of Musical Notes With WaveNet Autoencoders”, Engel et al 2017
  • “Tuning Recurrent Neural Networks With Reinforcement Learning”, Jaques et al 2017
  • “SampleRNN: An Unconditional End-To-End Neural Audio Generation Model”, Mehri et al 2016
  • “WaveNet: A Generative Model for Raw Audio”, Oord et al 2016
  • “The Abc Music Standard 2.1: §3.1.1: X:—Reference Number”, Walshaw2011
  • “Staring Emmy Straight in the Eye—And Doing My Best Not to Flinch”, Hofstadter & Cope2001
  • “Connectionist Music Composition Based on Melodic, Stylistic, and Psychophysical Constraints [Technical Report CU-CS–495–90]”, Mozer1990
  • “DarwinTunes”
  • “Ai-Forever/music-Composer”
  • “Autoregressive Long-Context Music Generation With Perceiver AR”
  • “Will AI Take the Pleasure Out of Music?”
  • “Qualia Research Institute: The Musical Album of 2024 (v1)”
  • “Stream Uncoolbob Aka DarwinTunes”
  • “Curious about You”, translucentaudiosynthesis319 2024
  • “Sydney Misbehaving”, Whiton2024
  • “Waifu Synthesis: Real Time Generative Anime”
  • “Composing Music With Recurrent Neural Networks”
  • “Old Musicians Never Die. They Just Become Holograms.”
  • “‘It’s the Screams of the Damned!’ The Eerie AI World of Deepfake Music Music”
  • “Inside the Discord Where Thousands of Rogue Producers Are Making AI Music”
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