Potenza Andrea, Zaffaroni-Caorsi Valentina, Benocci Roberto, Guagliumi Giorgia, Fouani Jalal M, Bisceglie Alessandro, Zambon Giovanni
Department of Earth and Environmental Sciences, University of Milano-Bicocca, 20126 Milan, Italy.
Independent Researcher, 40132 Bologna, Italy.
Sensors (Basel). 2024 Jul 17;24(14):4642. doi: 10.3390/s24144642.
Eco-acoustic indices allow us to rapidly evaluate habitats and ecosystems and derive information about anthropophonic impacts. However, it is proven that indices' values and trends are not comparable between studies. These incongruences may be caused by the availability on the market of recorders with different characteristics and costs. Thus, there is a need to reduce these biases and incongruences to ensure an accurate analysis and comparison between soundscape ecology studies and habitat assessments. In this study, we propose and validate an audio recording equalization protocol to reduce eco-acoustic indices' biases, by testing three soundscape recorder models: Song Meter Micro, Soundscape Explorer Terrestrial and Audiomoth. The equalization process aligns the signal amplitude and frequency response of the soundscape recorders to those of a type 1 level meter. The adjustment was made in MATLAB R2023a using a filter curve generated comparing a reference signal (white noise); the measurements were performed in an anechoic chamber using 11 audio sensors and a type 1 sound level meter (able to produce a .WAV file). The statistical validation of the procedure was performed on recordings obtained in an urban and Regional Park (Italy) assessing a significant reduction in indices' biases on the Song Meter Micro and Audiomoth.
生态声学指标使我们能够快速评估栖息地和生态系统,并获取有关人为影响的信息。然而,事实证明,不同研究之间指标的数值和趋势无法进行比较。这些不一致可能是由市场上具有不同特性和成本的录音机的可用性导致的。因此,有必要减少这些偏差和不一致,以确保在声景生态学研究和栖息地评估之间进行准确的分析和比较。在本研究中,我们通过测试三种声景录音机型号:Song Meter Micro、Soundscape Explorer Terrestrial和Audiomoth,提出并验证了一种音频记录均衡协议,以减少生态声学指标的偏差。均衡过程将声景录音机的信号幅度和频率响应与1型声级计的信号幅度和频率响应对齐。使用通过比较参考信号(白噪声)生成的滤波器曲线,在MATLAB R2023a中进行调整;测量在消声室中使用11个音频传感器和1型声级计(能够生成.WAV文件)进行。该程序的统计验证是在意大利一个城市和区域公园获得的录音上进行的,评估结果表明Song Meter Micro和Audiomoth的指标偏差显著降低。