A Coding Guide to Google Research’s MSEB: Writing Sound Encoders to the Benchmark Contract and Scoring Them Across Classification, Clustering, Retrieval and Segmentation

MSEB is a Massive Sound Embedding Benchmark from Google Research that provides a standardized framework for evaluating audio encoding models across multiple task types: classification, clustering, retrieval, and segmentation. The benchmark defines a three-layer architecture consisting of types (the data shapes that all tasks use), encoders (the model interface that implementations must follow), and evaluators (task-specific assessment modules). This multi-task approach matters because different encoder designs can excel at different evaluation tasks, making a single benchmark metric insufficient to capture overall model quality. The tutorial demonstrates this by creating two deliberately different encoders, one measuring loudness and another measuring timbre, then running them through all four evaluators to show how their relative performance varies depending on which task is being evaluated.