Improving Self-supervised Audio Representation based on Contrastive Learning with Conformer Encoder

Self-supervised contrastive learning has drawn much attention due to its stunning performance in speech and audio processing. Recent works in music information retrieval (MIR) or audio classification tasks also adopted this paradigm. However, most existing MIR frameworks construct their pipeline with augmented-segmented training and use convolution-based architecture; therefore, the audio sequential correlations are collapsed. This paper proposes a method using a Conformer-based encoder-projector model to deal with this problem. The experiments conducted on various test instances showed that our model has competitive results compared to the state-of-the-art convolution model in normal-speed audio and superior performance in speed-perturbed experiments thanks to preserving the sequential context ratio distribution.