  In acoustic scene classification researches, audio segment is usually split
into multiple samples. Majority voting is then utilized to ensemble the results
of the samples. In this paper, we propose a punishment voting algorithm based
on the super categories construction method for acoustic scene classification.
Specifically, we propose a DenseNet-like model as the base classifier. The base
classifier is trained by the CQT spectrograms generated from the raw audio
segments. Taking advantage of the results of the base classifier, we propose a
super categories construction method using the spectral clustering. Super
classifiers corresponding to the constructed super categories are further
trained. Finally, the super classifiers are utilized to enhance the majority
voting of the base classifier by punishment voting. Experiments show that the
punishment voting obviously improves the performances on both the DCASE2017
Development dataset and the LITIS Rouen dataset.
