  Acoustic scene classification (ASC) and acoustic event detection (AED) are
different but related tasks. Acoustic events can provide useful information for
recognizing acoustic scenes. However, most of the datasets are provided without
either the acoustic event or scene labels. To utilize the acoustic event
information to improve the performance of ASC tasks, we present the cross-task
pre-training mechanism which utilizes acoustic event information from the
pre-trained AED model for ASC tasks. On the other hand, most of the models were
designed and implemented on platforms with rich computing resources, and the
on-device applications were limited. To solve this problem, we use model
distillation method to compress our cross-task model to enable on-device
acoustic scene classification. In this paper, the cross-task models and their
student model were trained and evaluated on two datasets: TAU Urban Acoustic
Scenes 2019 dataset and TUT Acoustic Scenes 2017 dataset. Results have shown
that cross-task pre-training mechanism can significantly improve the
performance of ASC tasks. The performance of our best model improved relatively
9.5% in the TAU Urban Acoustic Scenes 2019 dataset, and also improved 10% in
the TUT Acoustic Scenes 2017 dataset compared with the official baseline. At
the same time, the performance of the student model is much better than that of
the model without teachers.
