Computational-Musicology

Abstract This research uses Aquamosh (1998), the quadrilingual debut album by Plastilina Mosh (Spanish, English, French, Japanese; produced by Tom Rothrock and Rob Schnapf — Beck’s Odelay team), as an empirical falsification probe for distributional sentence embeddings. The album’s quadrilingual structure converts code-switching from anecdotal concern into a quantitative experiment: every language transition is a guaranteed lexical discontinuity, allowing us to dissociate topical continuity from surface form. Core Finding (CONFIRMED): In all five sentence-embedding architectures probed — OpenAI text-embedding-3-large (3072-dim, decoder), Google LaBSE (768-dim, encoder, parallel-corpus), BAAI BGE-M3 (1024-dim), multilingual-E5-large (1024-dim), and paraphrase-multilingual-MPNet (768-dim) — a language switch in consecutive lyric lines approximately doubles the probability of “window break” (the embedding similarity falling below a calibrated coherence threshold). Mean relative gap across models: 1.69×; range: 1.31× (E5) to 1.94× (OpenAI). Permutation tests against H₀ of language-rupture independence reject with z = +6.54 (OpenAI), z = +4.51 (LaBSE), both p < 10⁻⁴ over 10,000 simulations. Logistic regression with GEE clustered by track and controls for line position and anchor/successor languages yields OR = 3.99 [2.51, 6.36] for OpenAI (p < 0.001), OR = 2.52 [1.39, 4.57] for LaBSE (p = 0.002). LLM-as-judge against GPT-4o-mini shows OpenAI declares “rupture” while a sophisticated reader sees continuity 3.18× more often in switches than in same-language transitions (false-break rate 0.060 vs 0.191). ...

Abstract This research introduces Attention Windows, a novel framework for measuring the cognitive span required by listeners to follow lyrical narratives. How long can a theme persist before the lyrics shift to something new? Building on previous semantic embedding analyses of the Beatles and Pink Floyd, we develop a multi-method approach to quantify this narrative architecture across two iconic albums: The Dark Side of the Moon and Abbey Road. Core Finding (UNEXPECTED): The analysis reveals a systematic failure of distributional semantics to capture abstract thematic coherence in progressive rock. The Beatles exhibit significantly longer attention windows (μ = 0.57 lines, SD = 1.48) than Pink Floyd (μ = 0.25 lines, SD = 0.97) when measured with OpenAI’s text-embedding-ada-002 at its calibrated threshold (θ = 0.85). This counterintuitive result (p < 0.01, Cohen’s d = -0.24) exposes a fundamental limitation: transformer-based embeddings, trained on distributional statistics from web corpora, systematically privilege type-level lexical overlap (repeated tokens, n-grams) over token-level conceptual continuity (abstract themes expressed through synonymy, metaphor, and semantic field variation). The Beatles’ verse-chorus architecture creates high embedding similarity through verbatim repetition, while Pink Floyd’s through-composed approach—deploying varied metaphorical expressions of unified philosophical themes—produces orthogonal embedding vectors despite conceptual unity. This is not a quirk of ada-002 but a structural property of distributional semantics: co-occurrence statistics cannot distinguish “same theme, different words” from “different themes, same words.” ...