  The current methodology in tackling Acoustic Scene Classification (ASC) task
can be described in two steps, preprocessing of the audio waveform into log-mel
spectrogram and then using it as the input representation for Convolutional
Neural Network (CNN). This paradigm shift occurs after DCASE 2016 where this
framework model achieves the state-of-the-art result in ASC tasks on the
(ESC-50) dataset and achieved an accuracy of 64.5%, which constitute to 20.5%
improvement over the baseline model, and DCASE 2016 dataset with an accuracy of
90.0% (development) and 86.2% (evaluation), which constitute a 6.4% and 9%
improvements with respect to the baseline system. In this paper, we explored
the use of harmonic and percussive source separation (HPSS) to split the audio
into harmonic audio and percussive audio, which has received popularity in the
field of music information retrieval (MIR). Although works have been done in
using HPSS as input representation for CNN model in ASC task, this paper
further investigate the possibility on leveraging the separated harmonic
component and percussive component by curating 2 CNNs which tries to understand
harmonic audio and percussive audio in their natural form, one specialized in
extracting deep features in time biased domain and another specialized in
extracting deep features in frequency biased domain, respectively. The deep
features extracted from these 2 CNNs will then be combined using bilinear
pooling. Hence, presenting a two-stream time and frequency CNN architecture
approach in classifying acoustic scene. The model is being evaluated on DCASE
2019 sub task 1a dataset and scored an average of 65% on development dataset,
Kaggle Leadership Private and Public board.
