Name
Abstract | ||
|---|---|---|
Acoustic Event Detection plays an important role for computational acoustic scene analysis. Although we would face with a sound overlapping problem in a real situation, conventional methods do not consider the problem enough. In this paper, we propose a new overlapped acoustic event detection technique combined a source separation technique of Non-negative Matrix Factorization with shared basis vectors and a deep neural network based acoustic model to improve the detection performance. Our approach showed 20.0% absolute higher performance than the best result achieved in the D-CASE 2012 challenge on the frame based F-measure. |
Year | Venue | Field |
|---|---|---|
2017 | IEEE Global Conference on Consumer Electronics | Mel-frequency cepstrum,Pattern recognition,Computer science,Matrix decomposition,Euclidean distance,Feature extraction,Non-negative matrix factorization,Artificial intelligence,Hidden Markov model,Source separation,Acoustic model |
DocType | ISSN | Citations |
Conference | 2378-8143 | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazumasa Yamamoto | 1 | 33 | 7.58 |
| Chikara Ishikawa | 2 | 0 | 0.34 |
| Koya Sahashi | 3 | 0 | 0.34 |
| Seiichi Nakagawa | 4 | 598 | 104.03 |