Abstract

We introduce the String Ensemble Chords Dataset (SECD), a large-scale controlled compositional audio dataset comprising 287,088 harmonic-interval and chord instances constructed through additive superposition of professionally recorded isolated string notes from the Philharmonia Orchestra into duo-, trio-, and quartet-like four-voice mixtures. Each mixture includes exact per-voice metadata for absolute pitch, dynamic marking, and playing technique, and the corpus is organised into six dataset groups covering harmonic intervals, triads, and seventh chords under loose and strict metadata-consistency conditions. To demonstrate dataset utility, we define four representative and reproducible reference benchmarks: ensemble size recognition, triad chord quality identification, per-instrument dynamics classification, and playing technique-family recognition. Baseline Audio Spectrogram Transformer (AST) models achieve test accuracies of 98.67%, 93.73%, 98.19%, and 99.39%, with corresponding macro-F1 scores of 98.64%, 93.73%, 98.01%, and 97.29%, under a complete-instance-disjoint, in-domain evaluation protocol. These results provide reproducible reference performance for the selected SECD tasks and demonstrate the corpus’s utility for controlled analysis of harmonic, timbral, dynamic, and textural attributes in classical string audio. The full SECD corpus is released through Zenodo as constructed audio mixtures with accompanying metadata, while the project GitHub repository provides the EXP1–EXP4 benchmark code, saved split definitions, and the mini-SECD demonstration package for lightweight reproducibility.

IPC Classification

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