  Unseen noise signal which is not considered in a model training process is
difficult to anticipate and would lead to performance degradation. Various
methods have been investigated to mitigate unseen noise. In our previous work,
an Instance-level Dynamic Filter (IDF) and a Pixel Dynamic Filter (PDF) were
proposed to extract noise-robust features. However, the performance of the
dynamic filter might be degraded since simple feature pooling is used to reduce
the computational resource in the IDF part. In this paper, we propose an
efficient dynamic filter to enhance the performance of the dynamic filter.
Instead of utilizing the simple feature mean, we separate Time-Frequency (T-F)
features as non-overlapping chunks, and separable convolutions are carried out
for each feature direction (inter chunks and intra chunks). Additionally, we
propose Dynamic Attention Pooling that maps high dimensional features as low
dimensional feature embeddings. These methods are applied to the IDF for
keyword spotting and speaker verification tasks. We confirm that our proposed
method performs better in unseen environments (unseen noise and unseen
speakers) than state-of-the-art models.
