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We introduce PodcastMix, a dataset formalizing the task of separatingbackground music and foreground speech in podcasts. We aim at defining abenchmark suitable for training and evaluating (deep learning) sourceseparation models. To that end, we release a large and diverse training datasetbased on programatically generated podcasts. However, current (deep learning)models can incur into generalization issues, specially when trained onsynthetic data. To target potential generalization issues, we release anevaluation set based on real podcasts for which we design objective andsubjective tests. Out of our experiments with real podcasts, we find thatcurrent (deep learning) models may have generalization issues. Yet, these canperform competently, e.g., our best baseline separates speech with a meanopinion score of 3.84 (rating "overall separation quality" from 1 to 5). Thedataset and baselines are accessible online.

PodcastMix: A dataset for separating music and speech in podcasts