We introduce the Expanded Groove MIDI dataset (E-GMD), an automatic drum transcription (ADT) dataset that contains 444 hours of audio from 43 drum kits, making it an order of magnitude larger than similar datasets, and the first with human-performed velocity annotations. We use E-GMD to optimize classifiers for use in downstream generation by predicting expressive dynamics (velocity) and show with listening tests that they produce outputs with improved perceptual quality, despite similar results on classification metrics. Via the listening tests, we argue that standard classifier metrics, such as accuracy and F-measure score, are insufficient proxies of performance in downstream tasks because they do not fully align with the perceptual quality of generated outputs.

翻译：我们引入了扩大的Groove MIDI数据集(E-GMD ), 这是一个自动的桶转录(ADT)数据集,包含来自43个桶包的444小时音频,使它成为比类似数据集更大的数量级,也是第一个具有人性化速度说明的。 我们使用E-GMD 优化分类器,用于下游生成,方法是预测表达动态(速度 ), 并通过监听测试显示,尽管在分类标准上取得了类似结果,它们仍能以更高的感知质量产生产出。 通过监听测试,我们认为,标准分类指标,如准确度和F计量分,不足以替代下游任务,因为它们不完全符合生成产出的感知质量。