基于NMF与CNN联合优化的声学场景分类

韦娟, 杨皇卫, 宁方立

Acoustic scene classification based on joint optimization of NMF and CNN

Juan WEI, Huangwei YANG, Fangli NING

表1 CNN模型结构

Table 1 CNN model structure

名称 CNN8 CNN10 CNN12 输入层 256×108×1 256×108×1 256×108×1 批归一化层, 卷积层 BN, 3×3@64 BN, 3×3@64 BN, 3×3@64 批归一化层, 激活层, 卷积层 BN, ReLu, 3×3@64 BN, ReLu, 3×3@64 BN, ReLu, 3×3@64 池化层 4×2AvgPooling 4×2AvgPooling 4×2AvgPooling 批归一化层, 激活层 卷积层 $\left( \begin{array}{l}{\rm{BN}}, {\rm{ReLu}}\\3 \times 3@128\end{array} \right) \times 2$ $\left( \begin{array}{l}{\rm{BN}}, {\rm{ReLu}}\\3 \times 3@128\end{array} \right) \times 2$ $\left( \begin{array}{l}{\rm{BN}}, {\rm{ReLu}}\\3 \times 3@128\end{array} \right) \times 2$ 池化层 4×2AvgPooling 4×2AvgPooling 4×2AvgPooling 批归一化层, 激活层 卷积层 $\left( \begin{array}{l}{\rm{BN}}, {\rm{ReLu}}\\3 \times 3@256\end{array} \right) \times 2$ $\left( \begin{array}{l}{\rm{BN}}, {\rm{ReLu}}\\3 \times 3@256\end{array} \right) \times 2$ $\left( \begin{array}{l}{\rm{BN}}, {\rm{ReLu}}\\3 \times 3@256\end{array} \right) \times 2$ 池化层 — 2×1AvgPooling 2×1AvgPooling 批归一化层, 激活层 卷积层 — — $\left( \begin{array}{l}{\rm{BN}}, {\rm{ReLu}}\\3 \times 3@512\end{array} \right) \times 2$ $\left( \begin{array}{l}{\rm{BN}}, {\rm{ReLu}}\\3 \times 3@512\end{array} \right) \times 2$ 池化层 — — 2×1AvgPooling 批归一化层, 激活层 卷积层 — — — — $\left( \begin{array}{l}{\rm{BN}}, {\rm{ReLu}}\\3 \times 3@1024\end{array} \right) \times 2$ 批归一化层, 激活层, 卷积层 BN, ReLu, 1×1@1024 批归一化层, 卷积层, 全局池化层 BN, 1×1@15, Global AvgPooling 全连接层, 输出层 Dense(15), Softmax