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This study evaluates expressive MIDI piano performances by integrating contextual embeddings from self-supervised music models, Aria and CLaMP3, into traditional attribute-scoped metrics. The authors highlight the limitations of conventional metrics that overlook note dependencies and propose a kernel-based approach to measure conditional distributional similarity without requiring note alignment. Their findings demonstrate that contextual embeddings can serve as effective perceptual proxies, aligning closely with human ratings and outperforming traditional evaluation methods.
Contextual embeddings can revolutionize how we evaluate expressive MIDI performances, aligning closely with human perception while overcoming traditional metric limitations.
Objective evaluation of expressive MIDI piano performances typically relies on attribute statistics such as timing, velocity, and duration of individual notes. However, these methods often disregard dependencies between notes, which poses a potential limitation in assessing the similarity between two sets of performances. In generative applications, the wide variety of expressive attributes makes it difficult to aggregate them into a single scalar metric for model selection. In this work, we reexamine attribute-scoped metrics and explore the perceptual properties of contextual embeddings from self-supervised symbolic music models, Aria and CLaMP3. Results from our listening study indicate that these models can be used as perceptual proxies, showing agreement with per-sample human ratings on par with traditional metrics. To measure conditional distributional similarity, we adapt Kernel Audio Distance to the symbolic music domain. Unlike Pearson correlation and reconstruction error, kernel-based methods on contextual embeddings do not require note alignment and are sensitive to contextual perturbations. To facilitate reproducibility, we release Pereval, an open-source library that integrates performance evaluation utilities, including both attribute-scoped and deep feature metrics.
