Effective Strategies for Automatic Analysis of Acoustic Signals in Long-Term Monitoring
Open accessAbstract
Hydrophones used in Passive Acoustic Monitoring generate vast amounts of data, with the storage requirements for raw signals dependent on the sampling frequency, which limits the range of frequencies that can be recorded. Since the installation of these observatories is costly, it is crucial to maximize the utility of high-sampling-rate recordings to expand the range of survey types. However, storing these large datasets for long-term trend analysis presents significant challenges. This paper proposes an approach that reduces the data storage requirements by up to 85% while preserving critical information about Power Spectral Density and Sound Pressure Level. The strategy involves generating these key metrics from spectrograms, enabling both short-term (micro) and long-term (macro) studies. A proposal for efficient data processing is presented, structured in three steps: the first focuses on generating key metrics to replace space-consuming raw signals, the second addresses the treatment of these metrics for long-term studies, and the third outlines the creation of event detectors from the processed metrics. A comprehensive overview of the essential features for analyzing acoustic signals is provided, along with considerations for the future design of marine observatories. The necessary calculations and processes are detailed, demonstrating the potential of these methods to address the current data storage and processing limitations in long-term acoustic monitoring.
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Hybrid compression schemes now cut file sizes by up to 85% while preserving the spectral fidelity needed for research.
“Hybrid compression schemes now cut file sizes by up to 85% while preserving the spectral fidelity needed for research (Diego-Tortosa et al. 2025; S. B. Martin et al. 2021)”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Diego-Tortosa et al. 2025
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- Authors
- Diego-Tortosa, D., Bonanno, D., Bou-Cabo, M., Di Mauro, L. S., Idrissi, A., Lara, G., Riccobene, G., Sanfilippo, S., & Viola, S.
- Affiliations
- Laboratori Nazionali del Sud (LNS) - Istituto Nazionale di Fisica Nucleare (INFN), Via S. Sofia 62, 95125 Catania, Italy
- Istituto Nazionale di Fisica Nucleare (IT)
- Consejo Superior de Investigaciones Científicas (ES)
- ORCID
- Venue
- Journal of Marine Science and Engineering
- Publisher
- MDPI AG
- Published
- 2025-02-27
- Volume
- 13
- Issue
- 3
- Pages
- 454
- ISSN
- 2077-1312 (ISSN-L)
- OpenAlex
- W4407995618
- Cited by
- 12
- References
- 20
- Citations by year
- 2026: 6 · 2025: 6
- FWCI
- 5.75
- Type
- journal article
- Language
- en
- Update notices
- correction (2025-02-27)
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Marine Science and Engineering (publishedVersion, cc-by)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
- Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
- Keywords
- passive acoustic monitoring, hydrophone, acoustic signal analysis, data storage reduction, power spectral density, sound pressure level, spectrogram, long-term monitoring, marine observatory, event detection
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