Musical Intelligence — Cycle 17 cross-subject per-piece BOLD encoding artifacts (ds003720)
收藏DataCite Commons2026-05-05 更新2026-05-07 收录
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https://zenodo.org/doi/10.5281/zenodo.19744624
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资源简介：
Reproducibility bundle for Musical Intelligence v9.5.7 Cycle 17 cross-subject per-piece BOLD encoding analysis on ds003720 (Nakai 2021, 4 subjects, 720 music clips). Contains MI 26-D region activation map features, baseline encoder features (MERT-768, CLAP-music-512, Random-26/768), per-subject HRF-lagged per-clip BOLD vectors, and Python analysis scripts for shuffle-null inference, Ridge 5-fold CV encoding, and CKA architecture tests. Reproduces every numerical claim in the paper's §Results "Cross-subject per-piece encoding on ds003720" section without requiring access to the MI engine source code.
The four analyses included:- Deney 1: Shuffle-clip null (500 permutations per encoder per subject, Benjamini-Hochberg FDR over 24 tests).- Deney 2: Ridge regression 5-fold CV with nested inner alpha selection, top-5% voxel held-out Pearson r, Fisher-z mean across subjects.- Deney 3: Feature-level CKA (MI-full vs MI-naive, MERT, CLAP, Random-26/768).- Deney 3b: CKA vs per-subject BOLD (encoding-geometry test).
Headline results reproducible from this bundle: MI 4/4 subjects pass BH-FDR shuffle-null (effect +0.047); MI-naive 1/4; Random 0/4; MERT 4/4 (+0.054). Ridge held-out r: MI 0.165 vs MI-naive 0.084 (+93% architectural lift); vs Random-26 0.090 (+83% dimensionality-matched lift); MERT 0.221 at 30x more feature dimensions. CKA feature-level (MI-full, MI-naive) = 0.994 (literal R5 H2 floor Δ ≥ 0.02 fails honestly; dimensionality-confounded).
Engine IP note: MI engine source code remains under SRC9 Sonic Intelligence LLC copyright. This deposit is an artifact-only release — the numerical encoding analysis is fully reproducible from the deposited features + BOLD + scripts without engine access. Research-use source access is available upon email request to amace@bu.edu (see bundle DOWNLOAD.md). Paper-time engine state is pinned to git tag v9.5.3-paper-time-snapshot (SHA 5beaee5c) in the private SRC9 Musical Intelligence repository.
This reproducibility model follows NeurIPS artifact-track guidelines and industry-research conventions (e.g., Gemini tech reports, GPT-4 technical report).
Runtime: ~40 min CPU (Apple M2 or equivalent). No GPU required. Bundle integrity verified via MANIFEST.sha256 (38 files).
提供机构：
Zenodo
创建时间：
2026-04-24
