Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing models manipulate only synchronized music samples of the same duration, limiting their understanding of expressive timing. We introduce PianoKontext, a flow matching rendering model for classical piano music that generates variable-length performances in the latent space of a pretrained Music2Latent model. We synthesize MIDI scores into deadpan audio and employ Dynamic Time Warping (DTW) in the latent space to construct paired data for training. The aligned embeddings are concatenated in DiT blocks, allowing for a simple and effective learning of the dependencies between the score and performances. Audio samples are available at our demo page: <a href=\"https://realfolkcode.github.io/pianokontext_demo/\" rel=\"nofollow\">https://realfolkcode.github.io/pianokontext_demo/</a>.</p>\n","updatedAt":"2026-06-12T07:23:24.885Z","author":{"_id":"630b2b87cd26ad7f60d50c6a","avatarUrl":"/avatars/69c57c2eadb055fdc1ba61ec23e0aa6f.svg","fullname":"Dmitry","name":"realfolkcode","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8353796005249023},"editors":["realfolkcode"],"editorAvatarUrls":["/avatars/69c57c2eadb055fdc1ba61ec23e0aa6f.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.12282","authors":[{"_id":"6a2ad1dafdec76e893e7626c","user":{"_id":"630b2b87cd26ad7f60d50c6a","avatarUrl":"/avatars/69c57c2eadb055fdc1ba61ec23e0aa6f.svg","isPro":false,"fullname":"Dmitry","user":"realfolkcode","type":"user","name":"realfolkcode"},"name":"Dmitrii Gavrilev","status":"claimed_verified","statusLastChangedAt":"2026-06-12T07:12:06.289Z","hidden":false}],"publishedAt":"2026-06-10T00:00:00.000Z","submittedOnDailyAt":"2026-06-12T00:00:00.000Z","title":"PianoKontext: Expressive Performance Rendering from Deadpan Context","submittedOnDailyBy":{"_id":"630b2b87cd26ad7f60d50c6a","avatarUrl":"/avatars/69c57c2eadb055fdc1ba61ec23e0aa6f.svg","isPro":false,"fullname":"Dmitry","user":"realfolkcode","type":"user","name":"realfolkcode"},"summary":"Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing models manipulate only synchronized music samples of the same duration, limiting their understanding of expressive timing. We introduce PianoKontext, a flow matching rendering model for classical piano music that generates variable-length performances in the latent space of a pretrained Music2Latent model. We synthesize MIDI scores into deadpan audio and employ Dynamic Time Warping (DTW) in the latent space to construct paired data for training. The aligned embeddings are concatenated in DiT blocks, allowing for a simple and effective learning of the dependencies between the score and performances. Audio samples are available at our demo page: https://realfolkcode.github.io/pianokontext_demo/.","upvotes":1,"discussionId":"6a2ad1dafdec76e893e7626d","projectPage":"https://realfolkcode.github.io/pianokontext_demo","githubRepo":"https://github.com/realfolkcode/pianokontext","githubRepoAddedBy":"user","ai_summary":"PianoKontext generates variable-length piano performances by aligning MIDI scores with audio in latent space using DTW and DiT blocks.","ai_keywords":["flow matching","audio editing models","expressive timing","PianoKontext","latent space","Music2Latent","Dynamic Time Warping","DiT blocks"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","githubStars":1},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"630b2b87cd26ad7f60d50c6a","avatarUrl":"/avatars/69c57c2eadb055fdc1ba61ec23e0aa6f.svg","isPro":false,"fullname":"Dmitry","user":"realfolkcode","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2606/2606.12282.md","query":{}}">
PianoKontext: Expressive Performance Rendering from Deadpan Context
Published on Jun 10
· Submitted by Dmitry on Jun 12 Abstract
PianoKontext generates variable-length piano performances by aligning MIDI scores with audio in latent space using DTW and DiT blocks.
Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing models manipulate only synchronized music samples of the same duration, limiting their understanding of expressive timing. We introduce PianoKontext, a flow matching rendering model for classical piano music that generates variable-length performances in the latent space of a pretrained Music2Latent model. We synthesize MIDI scores into deadpan audio and employ Dynamic Time Warping (DTW) in the latent space to construct paired data for training. The aligned embeddings are concatenated in DiT blocks, allowing for a simple and effective learning of the dependencies between the score and performances. Audio samples are available at our demo page: https://realfolkcode.github.io/pianokontext_demo/.
Community
Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing models manipulate only synchronized music samples of the same duration, limiting their understanding of expressive timing. We introduce PianoKontext, a flow matching rendering model for classical piano music that generates variable-length performances in the latent space of a pretrained Music2Latent model. We synthesize MIDI scores into deadpan audio and employ Dynamic Time Warping (DTW) in the latent space to construct paired data for training. The aligned embeddings are concatenated in DiT blocks, allowing for a simple and effective learning of the dependencies between the score and performances. Audio samples are available at our demo page: https://realfolkcode.github.io/pianokontext_demo/.
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Cite arxiv.org/abs/2606.12282 in a model README.md to link it from this page.
Cite arxiv.org/abs/2606.12282 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2606.12282 in a Space README.md to link it from this page.
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