Name
Title | ||
|---|---|---|
Location-Independent Multi-Channel Acoustic Scene Classification Using Blind Dereverberation, Blind Source Separation, and Model Ensemble |
Abstract | ||
|---|---|---|
This paper presents a location-independent multi-channel acoustic scene classification (ASC) system that avoids spatial overfitting. Generally, ASC suffers from noise and reverberation in real environments. In addition, the ASC performance is decreased by overfitting a dataset, which is the result of learning from acoustic transfer functions enclosed in the dataset. To resolve these problems, we present a location-independent multi-channel ASC system using blind dereverberation, blind sound source separation, pre-trained model-based classifiers, and model ensemble. Experimental results on the DCASE 2018 Task 5 dataset indicate that the proposed system, with an F1 score of 88.4%, outperforms the baseline system. Also, the results indicate that although no one specific function improves the performance dramatically, all functions complement each other through the model ensemble. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/APSIPAASC47483.2019.9023059 | Asia-Pacific Signal and Information Processing Association Annual Summit and Conference |
Keywords | DocType | ISSN |
acoustic scene classification,blind dereverberation,blind source separation,pretrained model,model ensemble | Conference | 2309-9402 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryo Tanabe | 1 | 14 | 4.78 |
| Takashi Endo | 2 | 29 | 7.78 |
| Yuki Nikaido | 3 | 0 | 0.34 |
| Kenji Ichige | 4 | 0 | 0.68 |
| Nguyen Phong | 5 | 0 | 0.34 |
| Yohei Kawaguchi | 6 | 25 | 9.48 |
| Koichi Hamada | 7 | 2 | 1.64 |