PSLA: Improving Audio Event Classification with Pretraining, Sampling, Labeling, and Aggregation

Yuan Gong, Yu-An Chung, James Glass

Audio event classification is an active research area and has a wide range of applications. Since the release of AudioSet, great progress has been made in advancing the classification accuracy, which mostly comes from the development of novel model architectures and attention modules. However, we find that appropriate training techniques are equally important for building audio event classification models with AudioSet, but have not received the attention they deserve. To fill the gap, in this work, we present PSLA, a collection of training techniques that can noticeably boost the model accuracy including ImageNet pretraining, balanced sampling, data augmentation, label enhancement, model aggregation and their design choices. By training an EfficientNet with these techniques, we obtain a model that achieves a new state-of-the-art mean average precision (mAP) of 0.474 on AudioSet, outperforming the previous best system of 0.439.

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