基于稀疏编码和卷积神经网络的声场景分类

Yong Tang, Anqin Lu, Z. Liu, Y. Leng, Rongyan Wang, Chengli Sun, Jiande Sun, Chan Lin, Weiwei Zhao, Wenjing Li
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引用次数: 0

摘要

CNN是目前广泛应用于声学场景分类的一种模型。在深度学习技术得到广泛应用之前,稀疏编码是声学分类领域中非常流行的一种模型。本文将这两种模型结合起来进行声场景分类。具体来说,将校正后的基于稀疏表示的分数与CNN分类模型得到的分数融合进行分类。在TUT声学场景2017数据集和LITIS Rouen数据集上的实验结果表明,该算法可以很好地利用稀疏编码和CNN的分类能力。
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Acoustic Scene Classification Based on Sparse Coding and Convolutional Neural Networks
CNN is a model which is currently widely used in acoustic scene classification. Sparse coding is a model which used to be very popular in acoustic classification field before deep learning technology is widely used. In this paper we combine these two models for acoustic scene classification. Specifically, the calibrated sparse representation based score is fused with the score obtained through CNN classification model for classification. Experimental results on TUT acoustic scenes 2017 dataset and LITIS Rouen dataset show that the proposed algorithm can make good use of the classification abilities of sparse coding and CNN.
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