Recently, 2D convolution has been found unqualified in sound event detection (SED). It enforces translation equivariance on sound events along frequency axis, which is not a shift-invariant dimension. To address this issue, dynamic convolution is used to model the frequency dependency of sound events. In this paper, we proposed the first full-dynamic method named \emph{full-frequency dynamic convolution} (FFDConv). FFDConv generates frequency kernels for every frequency band, which is designed directly in the structure for frequency-dependent modeling. It physically furnished 2D convolution with the capability of frequency-dependent modeling. FFDConv outperforms not only the baseline by 6.6\% in DESED real validation dataset in terms of PSDS1, but outperforms the other full-dynamic methods. In addition, by visualizing features of sound events, we observed that FFDConv could effectively extract coherent features in specific frequency bands, consistent with the vocal continuity of sound events. This proves that FFDConv has great frequency-dependent perception ability.

翻译：近期研究发现，二维卷积在声音事件检测（SED）中表现欠佳。其沿频率轴对声音事件施加平移等变性，而频率轴并非平移不变维度。为解决该问题，研究者采用动态卷积建模声音事件的频率依赖性。本文提出首个全动态方法——全频动态卷积（FFDConv）。FFDConv为每个频带生成频率核，通过结构设计直接实现频率依赖建模，从物理层面赋予二维卷积频率依赖建模能力。在DESED真实验证数据集上，FFDConv的PSDS1指标不仅较基线模型提升6.6%，更优于其他全动态方法。此外，通过可视化声音事件特征，我们观察到FFDConv能有效提取特定频带的相干特征，这与声音事件的发声连续性相吻合，充分证明其具备优异的频率依赖感知能力。