Audio Modeling Through the Lens of Epiplexity

Sound contains rich information about the surrounding environment and has become an increasingly important modality for machine learning systems that aim to understand, detect and respond to real-world events. While considerable progress has been made in audio modeling, generalization across domains remains difficult because of limited yet highly diverse training datasets, variation in recording conditions, and other factors. This motivates the search for datasets and processing techniques that support generalization across domains. We hypothesize that the recently introduced concept of epiplexity, the amount of structural information in a dataset which can be learned by a computationally bound observer, offers a way to assess how these datasets and processing techniques contribute to model generalization. Prior work on text, images, and video has found that models trained on datasets with higher epiplexity seems to generalize better to unseen downstream tasks (Finzi et al., 2026). This opens the door for understanding how to select datasets that support training more generalizable models. However, the behavior of epiplexity in the audio domain remains unexplored. We aim to use epiplexity to better understand what kinds of audio datasets can lead to improvement generalization in models. We measure the epiplexity of over 40 different audio datasets and the correlations between the epiplexity of 12 training datasets and downstream model performances over three tokenizers: DAC, EnCodec, and X-Codec. We find that while tokenizers historically known to produce better performance yield lower epiplexity, for a fixed tokenizer, epiplexity is usually weakly correlated to improved model performance. We contribute a method for computing epiplexity and conditional epiplexity for audio datasets using PyTorch. Marc Finzi, Shikai Qiu, Yiding Jiang, Pavel Izmailov, J. Zico Kolter, and Andrew Gordon Wilson. From entropy to epiplexity: Rethinking information for computationally bounded intelligence, 2026. URL https://arxiv.org/abs/2601.03220.