  In this paper, we propose a method for incremental learning of two distinct
tasks over time: acoustic scene classification (ASC) and audio tagging (AT). We
use a simple convolutional neural network (CNN) model as an incremental learner
to solve the tasks. Generally, incremental learning methods catastrophically
forget the previous task when sequentially trained on a new task. To alleviate
this problem, we propose independent learning and knowledge distillation (KD)
between the timesteps in learning. Experiments are performed on TUT 2016/2017
dataset, containing 4 acoustic scene classes and 25 sound event classes. The
proposed incremental learner first solves the ASC task with an accuracy of
94.0%. Next, it learns to solve the AT task with an F1 score of 54.4%. At the
same time, its performance on the previous ASC task decreases only by 5.1
percentage points due to the additional learning of the AT task.
