The Note-Chord-Voice Framework: Structured Source Separation and Causal Inference for EV Charging Data

[2608.14756] The Note-Chord-Voice Framework: Structured Source Separation and Causal Inference for EV Charging Data Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2608.14756 (eess) [Submitted on 14 Aug 2026] Title: The Note-Chord-Voice Framework: Structured Source Separation and Causal Inference for EV Charging Data Authors: Jiajie Chen , Jinfeng Li View a PDF of the paper titled The Note-Chord-Voice Framework: Structured Source Separation and Causal Inference for EV Charging Data, by Jiajie Chen and Jinfeng Li View PDF HTML (experimental) Abstract: Real-world EV charging data exhibit three interlocking pathologies: hardware fragmentation (network timeouts and billing resets split sessions), physical violations (independent energy/duration models produce impossible states like 50 kWh in 10 min on a 7 kW charger), and collider bias (clustering on post-treatment outcomes opens backdoor paths for price elasticity). We propose the Note-Chord-Voice framework, a music-inspired, axiom-driven pipeline that separates data cleaning (Repair Chords), structural pattern discovery (Harmonic Chords), descriptive source separation (NMF Voices), and causal inference into distinct, falsifiable stages. Key innovations: (i) falsification gates (A1-A5, G3, G10) that test data suitability before modeling; (ii) Gamma-initialized NMF with input rescaling for convergence stability from STL decomposition; (iii) tag-based coupon grading (A/B/C/D) to isolate quasi-random treatment from night-time confounders and targeted promotions; (iv) separate per-voice OLS to avoid simplex collinearity; (v) Foote novelty curves for structural regime detection. Applied to the Jiangmen dataset (495,707 sessions, 20 stations, from July 2024 to March 2025), all core axioms pass except G3 (no strong 168 h cycle). NMF achieves R^2=0.9921; the physically constrained duration model yields aggregate R^2=0.5409. Two voices are price-sensitive (beta = -11 to -14 min, p<0.001), of which one is stable (Voice 3, beta=-14.16) and one treatment-driven (Voice 1, beta=-11.10); only the stable voice supports causal claims. Counterfactual simulation shows targeting discounts to price-sensitive voices recovers 52.8% of discount expenditures (~0.85M CNY/year); restricting to the single stable price-sensitive voice yields a more conservative estimate. Comments: 30 pages, 10 figures Subjects: Signal Processing (eess.SP) ; Machine Learning (cs.LG); Sound (cs.SD) Cite as: arXiv:2608.14756 [eess.SP] (or arXiv:2608.14756v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.14756 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jinfeng Li [ view email ] [v1] Fri, 14 Aug 2026 07:36:50 UTC (663 KB) Full-text links: Access Paper: View a PDF of the paper titled The Note-Chord-Voice Framework: Structured Source Separation and Causal Inference for EV Charging Data, by Jiajie Chen and Jinfeng Li View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: cs cs.LG cs.SD eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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