论文标题
Dcase 2022挑战任务4
Frequency Dependent Sound Event Detection for DCASE 2022 Challenge Task 4
论文作者
论文摘要
尽管其他领域上的许多深度学习方法已应用于声音事件检测（SED），但到目前为止，该方法和SED的原始域之间的差异尚未得到适当考虑。由于SED使用具有二维（时间和频率）的音频数据进行输入，因此对这两个维度的彻底理解对于从SED上其他域的方法应用至关重要。先前的工作证明，这些方法在频率维度上的地址在SED中特别有力。通过应用滤波器和频率动态卷积这些是提出频率依赖的方法以增强SED性能，我们提交的模型达到了0.4704的最佳PSDS1，最佳PSDS2和0.8224的最佳PSDS2。
While many deep learning methods on other domains have been applied to sound event detection (SED), differences between original domains of the methods and SED have not been appropriately considered so far. As SED uses audio data with two dimensions (time and frequency) for input, thorough comprehension on these two dimensions is essential for application of methods from other domains on SED. Previous works proved that methods those address on frequency dimension are especially powerful in SED. By applying FilterAugment and frequency dynamic convolution those are frequency dependent methods proposed to enhance SED performance, our submitted models achieved best PSDS1 of 0.4704 and best PSDS2 of 0.8224.
