  Environmental sound analysis is currently getting more and more attentions.
In the domain, acoustic scene classification and acoustic event classification
are two closely related tasks. In this letter, a two-stage method is proposed
for the above tasks. In the first stage, a mixup based MTL solution is proposed
to classify both tasks in one single convolutional neural network. Artificial
multi-label samples are used in the training of the MTL model, which are mixed
up using existing single-task datasets. The multi-task model obtained can
effectively recognize both the acoustic scenes and events. Compared with other
methods such as re-annotation or synthesis, the mixup based MTL is low-cost,
flexible and effective. In the second stage, the MTL model is modified into a
single-task model which is fine-tuned using the original dataset corresponding
to the specific task. By controlling the frozen layers carefully, the
task-specific high level features are fused and the performance of the single
classification task is further improved. The proposed method has confirmed the
complementary characteristics of acoustic scene and acoustic event
classifications. Finally, enhanced by ensemble learning, a satisfactory
accuracy of 84.5 percent on TUT acoustic scene 2017 dataset and an accuracy of
77.5 percent on ESC-50 dataset are achieved respectively.
