  Sequential audio event tagging can provide not only the type information of
audio events, but also the order information between events and the number of
events that occur in an audio clip. Most previous works on audio event sequence
analysis rely on connectionist temporal classification (CTC). However, CTC's
conditional independence assumption prevents it from effectively learning
correlations between diverse audio events. This paper first attempts to
introduce Transformer into sequential audio tagging, since Transformers perform
well in sequence-related tasks. To better utilize contextual information of
audio event sequences, we draw on the idea of bidirectional recurrent neural
networks, and propose a contextual Transformer (cTransformer) with a
bidirectional decoder that could exploit the forward and backward information
of event sequences. Experiments on the real-life polyphonic audio dataset show
that, compared to CTC-based methods, the cTransformer can effectively combine
the fine-grained acoustic representations from the encoder and coarse-grained
audio event cues to exploit contextual information to successfully recognize
and predict audio event sequences.
