“SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks”【5】的切入点在于:最近关于SELD的研究中,常使用CRNN模型,但是考虑到RNN的循环特性,很难将CRNN模型有效的在嵌入式硬件中实现,这不仅是因为CRNN的计算难以并行化处理,更是因为处理CRNN需要high memory bandwidth和large memory buff...
SELD-TCN: Sound Event Detection & Localization via Temporal Convolutional Network | Python w/ Tensorflow neural-network tensorflow keras convolutional-neural-networks audio-processing audio-recognition keras-tensorflow sound-event-detection direction-of-arrival seldnet seld-tcn Updated Oct 1, 2020 Python...
The proposed framework (SELD-TCN) outperforms the state-of-the-art SELDnet performance on four different datasets. Moreover, SELD-TCN achieves 4x faster training time per epoch and 40x faster inference time on an ordinary graphics processing unit (GPU).Karim Guirguis...
doi:10.23919/EUSIPCO47968.2020.9287716Karim GuirguisChristoph SchornAndre GuntoroSherif AbdulatifBin YangIEEEEuropean Signal Processing Conference
本发明属于故障检测技术领域,具体涉及一种基于改进SELDTCN网络的滚动轴承故障诊断方法,包括如下步骤:数据采集,数据集构建,数据增强,数据集划分,模型构建,模型评价;数据采集用于网络训练;数据集构建,构建可用于深度神经网络训练的数据集;数据增强,防止数据欠拟合与过拟合,增强模型泛化能力与鲁棒性;数据集划分,用于模型训练...
本发明属于故障检测技术领域,具体涉及一种基于改进SELDTCN网络的滚动轴承故障诊断方法,包括如下步骤:数据采集,数据集构建,数据增强,数据集划分,模型构建,模型评价;数据采集用于网络训练;数据集构建,构建可用于深度神经网络训练的数据集;数据增强,防止数据欠拟合与过拟合,增强模型泛化能力与鲁棒性;数据集划分,用于模型训练...