Deep neural networks for audio scene recognition

Y. Petetin, Cyrille Laroche, Aurélien Mayoue
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引用次数: 40

Abstract

These last years, artificial neural networks (ANN) have known a renewed interest since efficient training procedures have emerged to learn the so called deep neural networks (DNN), i.e. ANN with at least two hidden layers. In the same time, the computational auditory scene recognition (CASR) problem which consists in estimating the environment around a device from the received audio signal has been investigated. Most of works which deal with the CASR problem have tried to ind well-adapted features for this problem. However, these features are generally combined with a classical classi-ier. In this paper, we introduce DNN in the CASR ield and we show that such networks can provide promising results and perform better than standard classiiers when the same features are used.
音频场景识别的深度神经网络
最近几年,人工神经网络(ANN)重新引起了人们的兴趣,因为有效的训练程序已经出现,可以学习所谓的深度神经网络(DNN),即至少有两个隐藏层的人工神经网络。同时,研究了基于接收到的音频信号估计设备周围环境的计算听觉场景识别(CASR)问题。大多数处理CASR问题的工作都试图找到适合这个问题的特征。然而,这些特征通常与经典的经典相结合。在本文中,我们将深度神经网络引入CASR领域,并表明当使用相同的特征时,这种网络可以提供有希望的结果,并且比标准分类器表现得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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