Active Learning for Sound Event Classification by Clustering Unlabeled Data
Tutkimustuotos › › vertaisarvioitu
Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Kustantaja | IEEE |
| Sivut | 751-755 |
| ISBN (elektroninen) | 978-1-5090-4117-6 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 2017 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING - Kesto: 1 tammikuuta 1900 → 1 tammikuuta 2000 |
Julkaisusarja
| Nimi | |
|---|---|
| ISSN (elektroninen) | 2379-190X |
Conference
| Conference | IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING |
|---|---|
| Ajanjakso | 1/01/00 → 1/01/00 |
Tiivistelmä
This paper proposes a novel active learning method to save annotation effort when preparing material to train sound event classifiers. K-medoids clustering is performed on unlabeled sound segments, and medoids of clusters are presented to annotators for labeling. The annotated label for a medoid is used to derive predicted labels for other cluster members. The obtained labels are used to build a classifier using supervised training. The accuracy of the resulted classifier is used to evaluate the performance of the proposed method. The evaluation made on a public environmental sound dataset shows that the proposed method outperforms reference methods (random sampling, certainty-based active learning and semi-supervised learning) with all simulated labeling budgets, the number of available labeling responses. Through all the experiments, the proposed method saves 50%–60% labeling budget to achieve the same accuracy, with respect to the best reference method.