環境音分類使用大規模預訓練模型以及半監督式訓練之初步研究

A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning

隨著智慧裝置的應用日漸普及，環境音分類技術的研究也越加受到重視。本論文探究環境音分類使用大規模聲音預訓練模型來發展環境音分類方法；並且在假設標記資料匱乏的情境下，基於遷移學習(Transfer Learning)的概念下，使用近期被提出的FixMatch訓練演算法以及SpecAugment資料擴增技術來達到辦監督訓練的目的。在環境音分類標竿資料集UrbanSound8K的實驗顯示，我們所提出的方法能較現有的基礎方法有2.4%準確率提升。

With the widespread commercialization of smart devices, research on environmental sound classification has gained more and more attention in recent years. In this paper, we set out to make effective use of large-scale audio pretrained model and semi-supervised model training paradigm for environmental sound classification. To this end, an environmental sound classification method is first put forward, whose component model is built on top a large-scale audio pretrained model. Further, to simulate a low-resource sound classification setting where only limited supervised examples are made available, we instantiate the notion of transfer learning with a recently proposed training algorithm (namely, FixMatch) and a data augmentation method (namely, SpecAugment) to achieve the goal of semi-supervised model training. Experiments conducted on bench-mark dataset UrbanSound8K reveal that our classification method can lead to an accuracy improvement of 2.4% in relation to a current baseline method.