This work proposes bag-of-features deep learning models for acoustic scene classi?cation (ASC) – identifying recording locations by analyzing background sound. We explore the effect on classi?cation accuracy of various front-end feature extraction techniques, ensembles of audio channels, and patch sizes from three kinds of spectrogram. The back-end process presents a two-stage learning model with a pre-trained CNN (preCNN) and a post-trained DNN (postDNN). Additionally, data augmentation using the mixup technique is investigated for both the pre-trained and post-trained processes, to improve classi?cation accuracy through increasing class boundary training conditions. Our experiments on the 2018 Challenge on Detection and Classi?cation of Acoustic Scenes and Events - Acoustic Scene Classi?cation (DCASE2018-ASC) subtask 1A and 1B signi?cantly outperform the DCASE2018 reference implementation and approach state-of-the-art performance for each task. Results reveal that the ensemble of multi-spectrogram features and data augmentation is bene?cial to performance.
|Titel||AES International Conference on Audio Forensics, 2019|
|Publikationsstatus||Veröffentlicht - Juli 2019|
|Veranstaltung||2019 AES International Conference on Audio Forensics - Porto, Portugal|
Dauer: 18 Juni 2019 → 20 Juni 2019
|Konferenz||2019 AES International Conference on Audio Forensics|
|Zeitraum||18/06/19 → 20/06/19|
- Data Science