Features

Pereval integrates both attribute-scoped and deep feature metrics under one package. Metrics can be used for 1) assessing distributional discrepancies between two sets of MIDI piano performances and 2) asssigning perceptual pseudo-ratings to individual performances.

Attribute-scoped metrics:

• Inter-set correlation

• Intra-set correlation

• KL Divergence

Deep feature metrics:

• Fréchet Music Distance (FMD)

• Kernel Music Distance (KMD)

• Kernel Performance Distance (KPD)

Per-sample pseudo ratings:

• Mahalanobis Distance

• Relative Mahalanobis Distance

• Marginal Mahalanobis Distance

Note that correlation, FMD, KMD, and KPD can also be used for per-sample evaluation.

	Fidelity	Diversity	Alignment-free	Score-aware	Contextual	Per-sample
Reconstruction error	✅	❌	❌	✅	❌	✅
Inter-correlation	✅	❌	❌	✅	❌	✅
Intra-correlation	❌	✅	❌	✅	❌	❌
KL Divergence	✅	✅	✅	❌	❌	❌
Fréchet Music Distance	✅	✅	✅	❌	✅	✅
Kernel Music Distance	✅	✅	✅	❌	✅	✅
Kernel Performance Distance	✅	✅	✅	✅	✅	✅
Mahalanobis Distance	✅	❌	✅	✅	✅	✅
Relative Mahalanobis Distance	✅	❌	✅	✅	✅	✅
Marginal Mahalanobis Distance	✅	❌	✅	❌	✅	✅

Comparison of attribute-scoped and deep feature metrics for evaluating MIDI piano performances.

Deep feature metrics are calculated using the embeddings from pretrained self-supervised symbolic music models (Aria and CLaMP3). Pereval supports feature extraction from the Aria model. To integrate CLaMP3 embeddings, please install CLaMP3 separately.