  Environmental sound recognition (ESR) is an emerging research topic in audio
pattern recognition. Many tasks are presented to resort to computational models
for ESR in real-life applications. However, current models are usually designed
for individual tasks, and are not robust and applicable to other tasks.
Cross-task models, which promote unified knowledge modeling across various
tasks, have not been thoroughly investigated. In this article, we propose a
cross-task model for three different tasks of ESR: 1) acoustic scene
classification; 2) urban sound tagging; and 3) anomalous sound detection. An
architecture named SE-Trans is presented that uses attention mechanism-based
Squeeze-and-Excitation and Transformer encoder modules to learn the channelwise
relationship and temporal dependencies of the acoustic features. FMix is
employed as the data augmentation method that improves the performance of ESR.
Evaluations for the three tasks are conducted on the recent databases of
detection and classification of acoustic scenes and event challenges. The
experimental results show that the proposed cross-task model achieves
state-of-the-art performance on all tasks. Further analysis demonstrates that
the proposed cross-task model can effectively utilize acoustic knowledge across
different ESR tasks.
