End-to-end environmental sound classification using a 1D convolutional neural network
作者：
Highlights：
• A model based on deep learning for environmental sound classification is proposed.
• Model provides the trade-off between the audio length, accuracy and parameter space.
• Model omits the signal processing modules and learns representations from audio.
• Model has a small parameter space compared to other architectures in the literature.
• The frequency and magnitude responses of some of the learned filters are analyzed.
摘要
•A model based on deep learning for environmental sound classification is proposed.•Model provides the trade-off between the audio length, accuracy and parameter space.•Model omits the signal processing modules and learns representations from audio.•Model has a small parameter space compared to other architectures in the literature.•The frequency and magnitude responses of some of the learned filters are analyzed.
论文关键词：Convolutional neural network,Environmental sound classification,Deep learning,Gammatone filterbank
论文评审过程：Received 10 April 2019, Revised 1 June 2019, Accepted 19 June 2019, Available online 20 June 2019, Version of Record 28 June 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.040
