  The performance of an Acoustic Scene Classification (ASC) system is highly
depending on the latent temporal dynamics of the audio signal. In this paper,
we proposed a multiple layers temporal pooling method using CNN feature
sequence as in-put, which can effectively capture the temporal dynamics for an
entire audio signal with arbitrary duration by building direct connections
between the sequence and its time indexes. We applied our novel framework on
DCASE 2018 task 1, ASC. For evaluation, we trained a Support Vector Machine
(SVM) with the proposed Multi-Layered Temporal Pooling (MLTP) learned features.
Experimental results on the development dataset, usage of the MLTP features
significantly improved the ASC performance. The best performance with 75.28%
accuracy was achieved by using the optimal setting found in our experiments.
