Data Compression for Audio-based Smart Beekeeping

DOI

10.2991/978-94-6463-720-5_10How to use a DOI?

Keywords

Beehive Monitoring; Free Lossless Audio Codec; Mel-Frequency Cepstral Coefficients; Support Vector Machine

Abstract

This research addresses the challenges of audio data compression in beehive monitoring by exploring the feasibility and effectiveness of the Free Lossless Audio Codec. The study demonstrates that Free Lossless Audio Codec compression reduces resource consumption without compromising critical acoustic features or AI performance. The methodology integrates Free Lossless Audio Codec compression, feature extraction using Mel-Frequency Cepstral Coefficients to capture relevant acoustic characteristics, and machine learning models, specifically Support Vector Machines, to classify and analyze hive conditions. The results demonstrated that Free Lossless Audio Codec outperformed MPEG-1 Audio Layer 3 and uncompressed Waveform Audio File formats in maintaining the efficiency of audio signals and the integrity of critical acoustic features. Key metrics such as classifier accuracy, compression ratio, processing speed, and transmission speed were evaluated using data from multiple sources. These findings position Free Lossless Audio Codec as a highly effective solution for resource-efficient and reliable beehive monitoring systems.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Guangyu Shi
AU  - Lei Song
AU  - Iman Ardekani
PY  - 2025
DA  - 2025/06/30
TI  - Data Compression for Audio-based Smart Beekeeping
BT  - Proceedings of the Smart Sustainable Development Conference 2025 (SSD 2025)
PB  - Atlantis Press
SP  - 117
EP  - 130
SN  - 3005-155X
UR  - https://doi.org/10.2991/978-94-6463-720-5_10
DO  - 10.2991/978-94-6463-720-5_10
ID  - Shi2025
ER  -