Abstract
This paper proposes a machine learning system for identification of queen-less beehives by using audio signal enhancement methods and neural networks. In the proposed system, noisy audio signals captured from beehives are enhanced by using a Wiener filter; Improved Mel-frequency Cepstrum Coefficient (IRIFCC) of the enhanced signals are then extracted and fed to a neural network. The result shows that the application of the proposed filter can improve the classification accuracy by at least 12%. The classification accuracy depends on the SNR of the input audio signal.
| Original language | English |
|---|---|
| Title of host publication | 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) |
| Subtitle of host publication | proceedings |
| Place of Publication | Piscataway, NJ |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 57-63 |
| Number of pages | 7 |
| ISBN (Electronic) | 9789881476883 |
| ISBN (Print) | 9781728181301 |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand Duration: 7 Dec 2020 → 10 Dec 2020 |
Conference
| Conference | 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 |
|---|---|
| Country/Territory | New Zealand |
| City | Virtual, Auckland |
| Period | 7/12/20 → 10/12/20 |