Learning Jazz Pianist Style with Cross-Attention Conditioning
How it works The model We start from Aria (Bradshaw et al., ISMIR 2025; code), a 16-layer transformer pretrained on a large corpus of piano MIDI. Into each of its last eight layers we insert a cross-attention block: the music attends to a small learned embedding for the chosen pianist, four vectors per pianist. A learned gate scales what the block adds, starting at 0.1, so fine-tuning begins from Aria’s own behaviour and learns how much to listen.
- ▪How it works The model We start from Aria (Bradshaw et al., ISMIR 2025; code), a 16-layer transformer pretrained on a large corpus of piano MIDI.
- ▪Into each of its last eight layers we insert a cross-attention block: the music attends to a small learned embedding for the chosen pianist, four vectors per pianist.
- ▪A learned gate scales what the block adds, starting at 0.1, so fine-tuning begins from Aria’s own behaviour and learns how much to listen.
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| Original publisher | Github |
| Canonical URL | https://almostimplemented.github.io/jazz-pianist-style/ |
| Publication time | Mon, 05 Oct 2026 22:30:52 +0000 |
| Retrieval time | 2026-10-06T01:08:40.516Z |
| Last seen | 2026-10-06T01:08:40.516Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | VwegYmtAUCJV |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
How it works The model We start from Aria (Bradshaw et al., ISMIR 2025; code), a 16-layer transformer pretrained on a large corpus of piano MIDI. Into each of its last eight layers we insert a cross-attention block: the music attends to a small learned embedding for the chosen pianist, four vectors per pianist. A learned gate scales what the block adds, starting at 0.1, so fine-tuning begins from Aria’s own behaviour and learns how much to listen. Because the embedding is attended to at every step, the conditioning does not fade as generation goes on, the way a prompt prefix does.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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