Perpetual Dialogues: A Computational Analysis of Voice-Guitar Interaction in Carlos Paredes's Discography

2026-03-13

AI Summary

This study addresses the limitations of existing computational musicology approaches, which often rely on notated scores and struggle to analyze orally transmitted, improvisatory vocal and guitar interactions. Focusing on collaborative recordings featuring Carlos Paredes, the work proposes a score-free, general-purpose computational framework that integrates source separation, physics-informed harmonic modeling, and beat-level audio descriptors. By employing multi-scale correlation analysis and residual-driven detection of structural deviations, the method systematically uncovers interaction patterns among melody, harmony, and rhythm. Applied to eight recordings, it successfully identifies musical recombination events aligned with formal boundaries and textural shifts, revealing for the first time that expressive coordination exhibits piece-specific characteristics. The framework demonstrates robust effectiveness and generalizability under minimal manual annotation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Humans and AI: Crowd Sourcing and Human Computation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Computational musicology enables systematic analysis of performative and structural traits in recorded music, yet existing approaches remain largely tailored to notated, score-based repertoires. This study advances a methodology for analyzing voice-guitar interaction in Carlos Paredes's vocal collaborations - an oral-tradition context where compositional and performative layers co-emerge. Using source-separated stems, physics-informed harmonic modelling, and beat-level audio descriptors, we examine melodic, harmonic, and rhythmic relationships across eight recordings with four singers. Our commonality-diversity framework, combining multi-scale correlation analysis with residual-based detection of structural deviations, reveals that expressive coordination is predominantly piece-specific rather than corpus-wide. Diversity events systematically align with formal boundaries and textural shifts, demonstrating that the proposed approach can identify musically salient reorganizations with minimal human annotation. The framework further offers a generalizable computational strategy for repertoires without notated blueprints, extending Music Performance Analysis into oral-tradition and improvisation-inflected practices.

Problem

• computational musicology
• oral tradition
• voice-guitar interaction
• music performance analysis
• improvisation

Innovation

• source separation
• physics-informed harmonic modeling
• multi-scale correlation analysis
• residual-based structural deviation
• oral-tradition music analysis