  In the acoustic scene classification (ASC) task, an acoustic scene consists
of diverse sounds and is inferred by identifying combinations of distinct
attributes among them. This study aims to extract and cluster these attributes
effectively using an improved multiple-instance learning (MIL) framework for
ASC. MIL, known as a weakly supervised learning method, is a strategy for
extracting an instance from a bundle of frames composing an input audio clip
and inferring a scene corresponding to the input data using these unlabeled
instances. However, many studies pointed out an underestimation problem of MIL.
In this study, we develop a MIL framework more suitable for ASC systems by
defining instance-level labels and loss to extract and cluster instances
effectively. Furthermore, we design a fully separated convolutional module,
which is a lightweight neural network comprising pointwise, frequency-sided
depthwise, and temporal-sided depthwise convolutional filters. As a result,
compared to vanilla MIL, the confidence and proportion of positive instances
increase significantly, overcoming the underestimation problem and improving
the classification accuracy up to 11%. The proposed system achieved a
performance of 81.1% and 72.3% on the TAU urban acoustic scenes 2019 and 2020
mobile datasets with 139 K parameters, respectively. Especially, it achieves
the highest performance among the systems having under the 1 M parameters on
the TAU urban acoustic scenes 2019 dataset.
