Influence of recording devices and environmental noise on acoustic index scores: Implications for bird sound-based assessments
Open accessAbstract
Passive acoustic monitoring serves as a minimally invasive and effective method for biodiversity assessment, particularly in bird monitoring through the application of acoustic indices. However, the use of different recording devices and different types of environmental noise (e.g., rain, wind, stream, traffic noise) lead to signal distortions that affect the application of ecoacoustics indices. Currently, there are no established guidelines specifying the technical requirements for recording devices and the signal-to-noise ratio (SNR) threshold for accurate calculation of acoustic indices. To enhance the accuracy of acoustic indices in bird biodiversity assessments, this study investigated the impact of different recording devices and environmental noise (rain, wind, stream, traffic noise) on acoustic indices. In this study, we selected six acoustic indices: Acoustic Complexity Index, Acoustic Diversity Index, Acoustic Evenness Index, Bioacoustic Index, Acoustic Entropy Index, and Normalized Difference Soundscape Index, and we used four different recording devices to simultaneously record 104 h of bird-sound data at the same location. In addition, 44 h of noisy bird signals with different noise types and intensities were artificially synthesized for comparison. The sound data were then used to analyze the effects of the recording devices and environmental noise on the acoustic indices used for bird biodiversity assessment. Our results showed that (a) all six acoustic indices were affected by the recording device used; (b) each acoustic index had different sensitivities to different noise types; and (c) there was a SNR threshold above which the effect of noise on the acoustic index was negligible. This study provides recommendations for recording device selection and determines SNR thresholds for noisy bird signals, contributing to the refinement of protocols for acquiring and preprocessing signals in the application of acoustic indices for bird biodiversity assessment. These findings aim to establish standardized protocols for signal acquisition and preprocessing in future application of acoustic indices.
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Many signal-processing features are sensitive to background noise, weather conditions, and anthropogenic sounds, which can distort ecological interpretations.
“Many signal-processing features are also sensitive to background noise, weather conditions, and anthropogenic sounds, which can distort ecological interpretations”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Zhang et al. 2024
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- Authors
- Zhang, C., Zhang, Y., Zheng, X., Gao, X., & Hao, Z.
- Affiliations
- Guangzhou University (CN)
- Research Institute of Tropical Forestry (CN)
- Corresponding author
- Gao, Xinghui
- Hao, Zezhou
- Venue
- Ecological Indicators
- Publisher
- Elsevier BV
- Published
- 2024-02
- Volume
- 159
- Pages
- 111759
- ISSN
- 1470-160X (ISSN-L)
- OpenAlex
- W4391895562
- Cited by
- 25
- References
- 68
- Citations by year
- 2026: 11 · 2025: 10 · 2024: 4
- FWCI
- 10.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Ecological Indicators (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (32171520)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
- Physical Sciences › Environmental Science › Ecology › Avian ecology and behavior
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- passive acoustic monitoring, acoustic indices, recording equipment, environmental noise, signal-to-noise ratio, ecoacoustics, bird song, rain noise, traffic noise, signal preprocessing, acoustic complexity index
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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