Machine hearing of the environmental sound is one of the important issues in
the audio recognition domain. It gives the machine the ability to discriminate
between the different input sounds that guides its decision making. In this
work we exploit the self-supervised contrastive technique and a shallow 1D CNN
to extract the distinctive audio features (audio representations) without using
any explicit annotations.We generate representations of a given audio using
both its raw audio waveform and spectrogram and evaluate if the proposed
learner is agnostic to the type of audio input. We further use canonical
correlation analysis (CCA) to fuse representations from the two types of input
of a given audio and demonstrate that the fused global feature results in
robust representation of the audio signal as compared to the individual
representations. The evaluation of the proposed technique is done on both
ESC-50 and UrbanSound8K. The results show that the proposed technique is able
to extract most features of the environmental audio and gives an improvement of
12.8% and 0.9% on the ESC-50 and UrbanSound8K datasets respectively.

利用自监督对比技术和浅层 1D CNN 提取环境音频的显著特征，进一步使用规范相关分析（CCA）来融合来自给定音频的两种输入类型的表示，并证明融合的全局特征相对于单独的表示具有更强的鲁棒性。在 ESC-50 和 UrbanSound8K 上的评估表明，该技术能够提取环境声音的大部分特征，并分别在 ESC-50 和 UrbanSound8K 数据集上获得了 12.8％和 0.9％的改进。