We present in this paper a simple, yet efficient convolutional neural network
(CNN) architecture for robust audio event recognition. Opposing to deep CNN
architectures with multiple convolutional and pooling layers topped up with
multiple fully connected layers, the proposed network consists of only three
layers: convolutional, pooling, and softmax layer. Two further features
distinguish it from the deep architectures that have been proposed for the
task: varying-size convolutional filters at the convolutional layer and 1-max
pooling scheme at the pooling layer. In intuition, the network tends to select
the most discriminative features from the whole audio signals for recognition.
Our proposed CNN not only shows state-of-the-art performance on the standard
task of robust audio event recognition but also outperforms other deep
architectures up to 4.5% in terms of recognition accuracy, which is equivalent
to 76.3% relative error reduction.

本研究提出了一种简单而高效的卷积神经网络（CNN）架构，用于鲁棒的音频事件识别，并采用变尺寸的卷积滤波器和 1-max 池化方案等创新特性，在标准的鲁棒音频事件识别任务上表现不但表现出最新技术水准，并且在识别准确率上优于其他深度网络架构 4.5％， 相当于 76.3％的相对误差降低。