和平 sells，但谁的歌曲连接？基于大四金属激进流派的贝叶斯多层网络分析
Peace Sells, But Whose Songs Connect? Bayesian Multilayer Network Analysis of the Big 4 of Thrash Metal
AI总结：
本文提出了一种贝叶斯多层网络模型，用于分析激进金属乐队的歌曲相似性，揭示了音乐作品间的结构化连接和潜在社区。
摘要：
我们提出了一种贝叶斯框架用于多层歌曲相似性网络，并将其应用于“大四”激进金属（金属lica，slayer，megadeth，anthrax）的完整录音室曲目。从原始音频开始，我们构建了四个特征特定的层（响度，亮度，音调，节奏），并用歌曲外生信息增强它们，并将每个层表示为k最近邻图。然后我们拟合了一组具有全局和层特定基线的分层probit模型，节点和层特定的社会效应，双人协变量，以及替代形式的潜在结构（双线性，距离基底，和随机块社区），通过后验预测检查，辨别和校准指标（AUC，Brier分数，log-loss）和信息标准（DIC，WAIC）进行比较。在所有乐队中，最丰富的随机块规范获得了最佳的预测性能和后验预测拟合，同时揭示了稀疏但结构化的连接，可解释的协变量效应（特别是专辑成员和时间接近），以及跨越专辑和时代的潜在社区和枢纽。这些结果展示了贝叶斯多层网络模型如何帮助将高维音频和文本特征组织成连贯且具有音乐意义的模式。
英文摘要：
We propose a Bayesian framework for multilayer song similarity networks and apply it to the complete studio discographies of the "Big 4" of thrash metal (Metallica, Slayer, Megadeth, Anthrax). Starting from raw audio, we construct four feature-specific layers (loudness, brightness, tonality, rhythm), augment them with song exogenous information, and represent each layer as a k-nearest neighbor graph. We then fit a family of hierarchical probit models with global and layer-specific baselines, node- and layer-specific sociability effects, dyadic covariates, and alternative forms of latent structure (bilinear, distance-based, and stochastic block communities), comparing increasingly flexible specifications using posterior predictive checks, discrimination and calibration metrics (AUC, Brier score, log-loss), and information criteria (DIC, WAIC). Across all bands, the richest stochastic block specification attains the best predictive performance and posterior predictive fit, while revealing sparse but structured connectivity, interpretable covariate effects (notably album membership and temporal proximity), and latent communities and hubs that cut across albums and eras. Taken together, these results illustrate how Bayesian multilayer network models can help organize high-dimensional audio and text features into coherent, musically meaningful patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
