Acoustic Scene Classification using Single Frequency Filtering Cepstral Coefficients and DNN
Chandrasekhar Paseddula, Suryakanth V. Gangashetty · 2020
Various representations have been developed for acoustic scene classifications (ASC) task using spectral information. However, there is a wide gap in dealing with acoustic scene representations. In this paper, we propose to use a single frequency filtering (SFF) approach, which provides good temporal and spectral resolution at each instant. Single-frequency filtering cepstral coefficients (SFFCC) with deep neural network (DNN) model as the classifier is used for the experimentation on DCASE 2019 and DCASE 2018 Task 1, development data of subtasks A and B. From the conducted experiments on the development datasets, the usage of the SFFCC features significantly improved ASC performance. This approach has got 35thteam rank out of 46 submissions to the corresponding DCASE 2019 Task 1A challenge with a 52.6% classification accuracy on the evaluation dataset. Also, the effect of raw waveforms taken as features for ASC using DNNs was observed.