  We propose a method named AudioFormer,which learns audio feature
representations through the acquisition of discrete acoustic codes and
subsequently fine-tunes them for audio classification tasks. Initially,we
introduce a novel perspective by considering the audio classification task as a
form of natural language understanding (NLU). Leveraging an existing neural
audio codec model,we generate discrete acoustic codes and utilize them to train
a masked language model (MLM),thereby obtaining audio feature representations.
Furthermore,we pioneer the integration of a Multi-Positive sample Contrastive
(MPC) learning approach. This method enables the learning of joint
representations among multiple discrete acoustic codes within the same audio
input. In our experiments,we treat discrete acoustic codes as textual data and
train a masked language model using a cloze-like methodology,ultimately
deriving high-quality audio representations. Notably,the MPC learning technique
effectively captures collaborative representations among distinct positive
samples. Our research outcomes demonstrate that AudioFormer attains
significantly improved performance compared to prevailing monomodal audio
classification models across multiple datasets,and even outperforms
audio-visual multimodal classification models on select datasets.
Specifically,our approach achieves remarkable results on datasets including
AudioSet (2M,20K),and FSD50K,with performance scores of 53.9,45.1,and
65.6,respectively. We have openly shared both the code and models:
https://github.com/LZH-0225/AudioFormer.git.

Sound;Machine Learning;Audio and Speech Processing