中圖分類號： TP391
文獻標識碼： A
DOI： 10.20044/j.csdg.2097-1788.2022.02.015
引用格式： 劉偉，孫蒙，張玥，等. 正交約束型SincNet可微分前端及在音頻分類中的應用[J].網(wǎng)絡安全與數(shù)據(jù)治理，2022，41(2)：92-98.

Differentiable front-end of SincNet with orthogonal constrains and its application in audio classification

Abstract： In the audio scene classification task, the feature modeling layer learning process in the existing end-to-end models has shortcomings such as lack of constraints, lack of intuitive interpretation of learning results, and only applicable to specific back-end classification models. Based on the differentiable front-end of SincNet, this paper introduces orthogonal constraints to improve the performance of the solution of the overall network, tries to promote the interpretability of the learnt front-end, and adapts it to multiple back-end classifiers. The proposed new front-end is called Orthogonal SincNet(OrthSincNet). Therefore, the convolution kernels of OrthSincNet is expected to not only hold good properties on frequency responses like Mel filters but also to be able to tune the parameters adaptively for specific back-end classifiers. Experimental results on the official test set of UrbanSound8K showed that OrthSincNet improved the conventional Mel filter banks by 2.2% in average on 6 back-end classifiers.

Key words : differentiable front-end; orthogonal convolutions; SincNet; end-to-end classification network