  In this paper we investigate the importance of the extent of memory in
sequential self attention for sound recognition. We propose to use a memory
controlled sequential self attention mechanism on top of a convolutional
recurrent neural network (CRNN) model for polyphonic sound event detection
(SED). Experiments on the URBAN-SED dataset demonstrate the impact of the
extent of memory on sound recognition performance with the self attention
induced SED model. We extend the proposed idea with a multi-head self attention
mechanism where each attention head processes the audio embedding with explicit
attention width values. The proposed use of memory controlled sequential self
attention offers a way to induce relations among frames of sound event tokens.
We show that our memory controlled self attention model achieves an event based
F -score of 33.92% on the URBAN-SED dataset, outperforming the F -score of
20.10% reported by the model without self attention.

Audio and Speech Processing;Machine Learning;Sound