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Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation

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arxiv 2412.07948 v2 pith:V2ZFDJ2Y submitted 2024-12-10 cs.SD cs.AIcs.MMeess.AS

Jan Retkowski , Jakub Stępniak , Mateusz Modrzejewski This is my paper · ORCID
classification cs.SDcs.AIcs.MMeess.AS
keywords musicdistancesymbolicfrechetgenerativemetricaudioevaluation
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In this paper we introduce the Frechet Music Distance (FMD), a novel evaluation metric for generative symbolic music models, inspired by the Frechet Inception Distance (FID) in computer vision and Frechet Audio Distance (FAD) in generative audio. FMD calculates the distance between distributions of reference and generated symbolic music embeddings, capturing abstract musical features. We validate FMD across several datasets and models. Results indicate that FMD effectively differentiates model quality, providing a domain-specific metric for evaluating symbolic music generation, and establishing a reproducible standard for future research in symbolic music modeling.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BeatEdit: Symbolic Music Generation as Explicit Editing

    cs.SD 2026-07 conditional novelty 7.0 of 10

    Explicit edit operations on Beat encoding outperform AR and diffusion on music error correction, accompaniment editing, and segment completion while running under 100 ms.

  2. Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

    cs.SD 2026-07 conditional novelty 6.0 of 10

    Kernel distances on CLaMP3/Aria embeddings score expressive MIDI performances about as well as human listeners and catch contextual corruptions invisible to attribute statistics.

  3. CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages

    cs.SD 2025-02 conditional novelty 5.0 of 10

    A contrastive learning framework (CLaMP 3) aligns three music modalities with multilingual text, enabling text-to-music retrieval, cross-lingual retrieval for unseen languages, and emergent cross-modal retrieval.

  4. Unveiling Audio Deepfake Origins: A Deep Metric learning And Conformer Network Approach With Ensemble Fusion

    cs.SD 2025-06 conditional novelty 4.0 of 10

    An XLSR-Conformer system trained with Real Emphasis, Fake Dispersion, and multi-class N-pair loss reaches 95.6% in-domain and up to 44.8% out-of-domain source tracing accuracy on MLAAD, versus 83.4% and 26.5% for the ...

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