A Noise Reduction Strategy via Coprime-Spacing Subarrays for Biodiversity Acoustic Indices

CHEN Lei; XU Zhiyong; ZHAO Zhao

doi:10.11999/JEIT260237

Volume 48 Issue 9

Sep. 2026

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CHEN Lei, XU Zhiyong, ZHAO Zhao. A Noise Reduction Strategy via Coprime-Spacing Subarrays for Biodiversity Acoustic Indices[J]. Journal of Electronics & Information Technology, 2026, 48(9): 3943-3953. doi: 10.11999/JEIT260237

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CHEN Lei, XU Zhiyong, ZHAO Zhao. A Noise Reduction Strategy via Coprime-Spacing Subarrays for Biodiversity Acoustic Indices[J]. Journal of Electronics & Information Technology, 2026, 48(9): 3943-3953. doi: 10.11999/JEIT260237

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A Noise Reduction Strategy via Coprime-Spacing Subarrays for Biodiversity Acoustic Indices

doi: 10.11999/JEIT260237 cstr: 32379.14.JEIT260237

School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received Date: 2026-03-05
Accepted Date: 2026-05-15
Rev Recd Date: 2026-05-15

Available Online: 2026-06-03

Publish Date: 2026-09-18

Abstract

Abstract

Objective Acoustic indices are widely used for rapid biodiversity assessment and have received increasing attention in soundscape ecology. However, most commonly used acoustic indices are sensitive to background noise. Traditional single-channel noise reduction methods, such as spectral subtraction, high-pass filtering, and threshold detection, are often used as preprocessing steps to improve acoustic index calculation. However, when anthropogenic interference overlaps with biological sound signals in both the time and frequency domains, the denoising performance of these methods decreases sharply. Microphone-array-based spatio-temporal adaptive whitening filtering provides a feasible approach for suppressing directional interference. However, its non-uniform two-dimensional spatio-temporal amplitude response and target self-cancellation during unconstrained interference cancellation can distort the time-frequency distribution of biological sound signals. This distortion causes acoustic index values to deviate from their noise-free ideal values. To address this problem, this study proposes a noise reduction strategy via coprime-spacing subarrays for biodiversity acoustic indices. The proposed method suppresses directional anthropogenic interference while preserving the time-frequency distribution structure of biological sound signals as completely as possible. Methods A microphone-array-based spatio-temporal adaptive whitening filtering strategy is proposed. It is combined with the Frequency-dependent Acoustic Diversity Index (FADI), which is insensitive to fluctuations in the two-dimensional spatio-temporal amplitude response of the array. A noise-robust acoustic index, termed Adaptive Interference Cancellation-FADI (AIC-FADI), is then developed. Specifically, a non-uniform linear array is constructed using three microphones to form two dual-element subarrays with coprime spacing. This design uses the high spatial resolution of wide-spacing arrays to narrow the null width in the interference direction. It also reduces the physical implementation difficulty and mutual coupling effects associated with small-spacing arrays, which arise from the ultra-wideband characteristics of biological sound signals. Spatio-temporal adaptive whitening filtering is then applied separately to each coprime-spacing subarray. Two-dimensional nulls are adaptively formed within the interference support region, thereby suppressing directional anthropogenic interference in the analysis data before index calculation. A frequency-dependent adaptive thresholding scheme is then used to obtain the binary time-frequency spectrum of each subarray output. This scheme reduces the effect of gain differences along the frequency axis for a given direction. The high spatial resolution of wide-spacing arrays and the interleaved spatial aliasing nulls in the spatio-temporal frequency responses of the two coprime-spacing subarrays are then used for pointwise maximum fusion of the two binary time-frequency spectra. This process reconstructs the binary time-frequency distribution structure of biological sound signals outside the interference support region. A single binary time-frequency spectrum is obtained, in which biological sound signals are largely preserved and anthropogenic interference is substantially suppressed. Finally, the proportions of time-frequency bins assigned a value of 1 in each frequency band are calculated and input into the entropy function following the Acoustic Diversity Index (ADI) framework, yielding the final AIC-FADI value. Results and Discussions Simulation results show that AIC-FADI maintains numerical robustness down to –15 dB Signal-to-Interference-plus-Noise Ratio (SINR) (yellow line in Fig. 5). It substantially outperforms the single-channel FADI baseline and the ADI-based variants without coprime-subarray fusion evaluated in this paper, including AIC-FADI-s, AIC-FADI1, and AIC-FADI2. Field experiments further confirm that the proposed spatio-temporal adaptive whitening filtering effectively suppresses wideband interference in complex soundscapes and improves the SINR of the analyzed recordings. This improvement allows some weaker biological sound signals to exceed their corresponding frequency-dependent adaptive thresholds, thereby reducing missed detections of target signals. In addition, pointwise maximum fusion of the binary time-frequency spectra from the two coprime-spacing subarray outputs further reduces missed detections in AIC-FADI (Fig. 8). However, the field experiments also show that the interference suppression performance of AIC-FADI decreases for highly time-varying interference components. Conclusions This study addresses the reliable calculation of acoustic indices in complex soundscapes where directional anthropogenic interference overlaps with biological sound signals in both the time and frequency domains. A noise reduction strategy via coprime-spacing subarrays is proposed, and the noise-robust AIC-FADI method is developed. Simulation and field recording results show that spatio-temporal adaptive whitening filtering on each coprime-spacing subarray, followed by pointwise maximum fusion, provides both wideband interference suppression and target information fidelity in complex soundscapes with strong interference. AIC-FADI maintains numerical robustness down to –15 dB SINR and outperforms the single-channel FADI baseline and ADI-based variants without coprime-subarray fusion. The proposed method provides a feasible technical solution for extending the practical application scenarios and spatio-temporal coverage of biodiversity acoustic indices in human-dominated areas. However, this study considers only directional interference that is relatively stable or slowly time-varying. Its suppression performance decreases for highly time-varying or uncorrelated noise components. These issues should be addressed in future work using more targeted signal processing techniques to further improve acoustic index robustness in highly complex acoustic environments.
- Microphone array,
- Adaptive interference cancellation,
- Spatio-temporal adaptive filtering,
- Spatial aliasing effect mitigation,
- Acoustic index

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References(16)

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