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Unseen noise signal which is not considered in a model training process isdifficult to anticipate and would lead to performance degradation. Variousmethods have been investigated to mitigate unseen noise. In our previous work,an Instance-level Dynamic Filter (IDF) and a Pixel Dynamic Filter (PDF) wereproposed to extract noise-robust features. However, the performance of thedynamic filter might be degraded since simple feature pooling is used to reducethe computational resource in the IDF part. In this paper, we propose anefficient 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 outfor each feature direction (inter chunks and intra chunks). Additionally, wepropose Dynamic Attention Pooling that maps high dimensional features as lowdimensional feature embeddings. These methods are applied to the IDF forkeyword spotting and speaker verification tasks. We confirm that our proposedmethod performs better in unseen environments (unseen noise and unseenspeakers) than state-of-the-art models.

Efficient dynamic filter for robust and low computational feature extraction