Automatic acoustic heterogeneity identification in transformed landscapes from Colombian tropical dry forests

Rendon, Nestor; Rodríguez‐Buriticá, Susana; Sánchez‐Giraldo, Camilo; Daza, Juan M.; Isaza, Claudia

doi:10.1016/j.ecolind.2022.109017

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…We found studies that use datasets from 24 countries. The country with the most publications referring to datasets collected in its territory is Australia with 16 studies [58] , [59] , [31] , [26] , [60] , [61] , [62] , [63] , [64] , [50] , [65] , [66] , [34] , [43] , [67] , [9] ; followed by the United States with 14 publications [68] , [48] , [69] , [70] , [71] , [10] , [72] , [35] , [73] , [74] , [55] , [75] , [76] , [23] ; Brazil with 13 publications [40] , [27] , [60] , [11] , [77] , [28] , [78] , [79] , [80] , [57] , [46] , [47] , [81] ; Costa Rica [12] , [40] , [25] , [45] , Malaysia [42] , [82] , [83] , [84] and the United Kingdom [22] , [85] , [32] , [86] with four publications each; Colombia [87] , [8] , [88] and France [85] , [89] , …”

Section: Resultsmentioning

confidence: 99%

“… Significative differences among analyzed sites [12] GMM SM2 recorded in a frequency of 5-min every 10 min for 5 days and stopping 5 days.

F1-Score = 0.92 [87] HMMS 521 audio recordings of species of whales.

acc from 0.82 (9 classes) to 1.00 (1–3 classes)

[51] KNN, AlexNet, VGG, Inception ResNet, DenseNet

Xeno-Canto, Tierstimmen and a private collection.

…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring

Nieto-Mora,

Rodríguez-Buritica,

Rodríguez-Marín

et al. 2023

Heliyon

Self Cite

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Section: Resultsmentioning

confidence: 99%

“… Significative differences among analyzed sites [12] GMM SM2 recorded in a frequency of 5-min every 10 min for 5 days and stopping 5 days.

F1-Score = 0.92 [87] HMMS 521 audio recordings of species of whales.

acc from 0.82 (9 classes) to 1.00 (1–3 classes)

[51] KNN, AlexNet, VGG, Inception ResNet, DenseNet

Xeno-Canto, Tierstimmen and a private collection.

…”

Section: Resultsmentioning

confidence: 99%

Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring

Nieto-Mora,

Rodríguez-Buritica,

Rodríguez-Marín

et al. 2023

Heliyon

Self Cite

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“…The frequency band was limited to 2-8 kHz for BI, and the signal was divided into 10 parts for NP. The computational tool in Python by Rendon et al (2022) was used to estimate ACI, BI and NP, while the SO index was estimated using the implementation provided by Rojas et al (2022).…”

Section: Acoustics Indicesmentioning

confidence: 99%

Acoustic animal identification using unsupervised learning

Guerrero

Bedoya²,

López

et al. 2023

Methods Ecol Evol

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1. Passive acoustic monitoring is usually presented as a complementary approach to monitoring wildlife communities and assessing ecosystem conditions. Automatic species detection methods support biodiversity monitoring and analysis by providing information on the presence-absence of species, which allows understanding the ecosystem structure. Therefore, different alternatives have been proposed to identify species. However, the algorithms are parameterized to identify specific species. Analysing multiple species would help to monitor and quantify biodiversity, as it includes the different taxonomic groups present in the soundscape.2. We present an unsupervised methodology for multi-species call recognition from ecological soundscapes. The proposal is based on a clustering algorithm, specifically the learning algorithm for multivariate data analysis (LAMDA) 3pi algorithm, which automatically suggests the number of clusters associated with the sonotypes. Emphasis was made on improving the segmentation of the audio to analyse the whole soundscape without parameterizing the algorithm according to each taxonomic group.3. To estimate the performance of our proposal, we used four datasets from different locations, years and habitats. These datasets contain sounds from the four major taxonomic groups that dominate terrestrial soundscapes (birds, amphibians, mammals and insects) in audible and ultrasonic spectra. The methodology presents performances between 75% and 96% in presence-absence species recognition.4. Using the clusters proposed by our methodology, the whole soundscape biodiversity was measured and compared with the estimate of four acoustic indices (ACI, NP, SO and BI). Our approach performs biodiversity assessments similar to acoustic indices with the advantage of providing information about acoustic communities without the need for prior knowledge of the species present in the audio recordings.

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“…Most of the literature evaluating the performance of acoustic indices focuses on temperate and tropical humid forests. The few studies on tropical dry forests are from Latin America (e.g., Rendon et al, 2022) and Africa (e.g., Rankin & Axel, 2017). Tropical dry forests, particularly in Asia, remain underrepresented in the field of ecology and ecoacoustics literature, especially concerning acoustic indices (Izaguirre & Ramírez‐Alán, 2018; Oliveira et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Measuring biodiversity with sound: How effective are acoustic indices for quantifying biodiversity in a tropical dry forest?

Kotian,

Biniwale,

Mourya

et al. 2024

Conservat Sci and Prac

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Large‐scale biodiversity conservation and management necessitate rapid assessment tools and technologies. Indices derived from passive acoustic data offer a novel solution for rapid biodiversity monitoring. Although these indices quantify vocalizing biodiversity at a site, previous studies indicate inconsistencies in the indices' performance across different biomes. We tested the efficacy of seven acoustic indices—acoustic complexity index, acoustic diversity index, bioacoustic index (BI), acoustic entropy index, total entropy (H), normalized difference soundscape index, and number of peaks in an understudied biome, tropical dry forests, in central India. We measured the relationship between every index and a combination of these indices with a biodiversity metric, avian species richness. We found a weak correlation between individual indices and species richness (0.00 ≤ R ≤ 0.35), while a combination of indices was comparatively better at predicting species richness (R2 = 0.54). Although BI performed better than all other indices, our results indicate that acoustic indices do not accurately quantify avian species richness in this forest in central India. However, combining multiple indices increases their efficacy, limitedly. We recommend evaluating the efficiency of acoustic indices, especially in underrepresented habitats, before their application in avifauna‐based rapid acoustic surveys.

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Automatic acoustic heterogeneity identification in transformed landscapes from Colombian tropical dry forests

Cited by 10 publications

References 28 publications

Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring

Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring

Acoustic animal identification using unsupervised learning

Measuring biodiversity with sound: How effective are acoustic indices for quantifying biodiversity in a tropical dry forest?

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