Name
Title | ||
|---|---|---|
Generative Adversarial Network Based Acoustic Scene Training Set Augmentation and Selection Using SVM Hyper-Plane. |
Abstract | ||
|---|---|---|
Although it is typically expected that using a large amount of labeled training data would lead to improve performance in deep learning, it is generally difficult to obtain such DataBase (DB). In competitions such as the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge Task 1, participants are constrained to use a relatively small DB as a rule, which is similar to the aforementioned issue. To improve Acoustic Scene Classification (ASC) performance without employing additional DB, this paper proposes to use Generative Adversarial Networks (GAN) based method for generating additional training DB. Since it is not clear whether every sample generated by GAN would have equal impact in classification performance, this paper proposes to use Support Vector Machine (SVM) hyper plane for each class as reference for selecting samples, which have class discriminative information. Based on the crossvalidated experiments on development DB, the usage of the generated features could improve ASC performance. |
Year | Venue | DocType |
|---|---|---|
2017 | DCASE | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seongkyu Mun | 1 | 13 | 3.08 |
| SangWook Park | 2 | 0 | 4.06 |
| David K. Han | 3 | 216 | 27.96 |
| Hanseok Ko | 4 | 421 | 80.24 |