EchoLogger: sistema embarcado para cálculo do índice de riqueza acústica no monitoramento ambiental não invasivo
Resumo
Este artigo apresenta o EchoLogger, um sistema embarcado de baixo custo para monitoramento bioacústico ambiental. Baseado no ESP32, o dispositivo captura e processa áudio em tempo real, calculando o Índice de Riqueza Acústica (AR) para estimar a diversidade sonora. A metodologia inclui a comparação dos cálculos do EchoLogger com a biblioteca scikit-maad e um teste de campo para avaliar a viabilidade do processamento embarcado. Os resultados indicam alta precisão nos cálculos de mediana e entropia, com um MAE de 0,0429 para a mediana e 0,0776 para a entropia, demonstrando que o processamento em tempo real não compromete a qualidade dos dados. O estudo confirma a eficiência e acessibilidade do EchoLogger para monitoramento ambiental, destacando seu potencial em biodiversidade e conservação.Referências
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Bradfer-Lawrence, T., Duthie, B., Abrahams, C., Adam, M., Barnett, R. J., Beeston, A., Darby, J., Dell, B., Gardner, N., Gasc, A., Heath, B., Howells, N., Janson, M., Kyoseva, M.-V., Luypaert, T., Metcalf, O. C., Nousek-McGregor, A. E., Poznansky, F., Ross, S. R. P.-J., Sethi, S., Smyth, S., Waddell, E., and Froidevaux, J. S. P. (2024). The acoustic index user’s guide: A practical manual for defining, generating and understanding current and future acoustic indices. Methods in Ecology and Evolution.
Buxton, R. T., McKenna, M. F., Clapp, M., Meyer, E., Stabenau, E., Angeloni, L. M., Crooks, K., and Wittemyer, G. (2018). Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity. Conservation Biology, 32(5):1174–1184.
Colonna, J. G., Carvalho, J. R. H., and Rosso, O. A. (2020). Estimating ecoacoustic activity in the amazon rainforest through information theory quantifiers. PLOS ONE, 15(7).
Depraetere, M., Pavoine, S., Jiguet, F., Gasc, A., Duvail, S., and Sueur, J. (2012). Monitoring animal diversity using acoustic indices: Implementation in a temperate woodland. Ecological Indicators, 13(1):46–54.
Farina, A. (2018). Perspectives in ecoacoustics: A contribution to defining a discipline. Journal of Ecoacoustics, 2:TRZD5I.
Hill, A. P., Prince, P., Piña Covarrubias, E., Doncaster, C. P., Snaddon, J. L., and Rogers, A. (2018). Audiomoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment. Methods in Ecology and Evolution, 9(5):1199–1211.
Kadish, D. and Stoy, K. (2022). Bioacoustic index tool: long-term biodiversity monitoring using on-sensor acoustic index calculations. Bioacoustics, 31(3):348–378.
Kaggle, Xeno-canto, and Contributors, V. (2022). Birdclef 2022 - bird species recognition in soundscape recordings. Kaggle Competition Dataset.
Mennill, D. J. (2024). Field tests of small autonomous recording units: an evaluation of in-person versus automated point counts and a comparison of recording quality. Bioacoustics, 33(2):157–177.
Towsey, M., Wimmer, J., Williamson, I., and Roe, P. (2014). The use of acoustic indices to determine avian species richness in audio-recordings of the environment. Ecological Informatics, 21:110–119. Ecological Acoustics.
Ulloa, J. S., Haupert, S., Latorre, J. F., Aubin, T., and Sueur, J. (2021). scikit-maad: An open-source and modular toolbox for quantitative soundscape analysis in python. Methods in Ecology and Evolution, 12(12):2334–2340.
Berndt, D. J. and Clifford, J. (1994). Using dynamic time warping to find patterns in time series. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS’94, page 359–370. AAAI Press.
Bogdanov, D., Wack, N., Gómez, E., Gulati, S., Herrera, P., Mayor, O., Roma, G., Salamon, J., Zapata, J., and Serra, X. (2013). Essentia: an open-source library for sound and music analysis. In Proceedings of the 21st ACM international conference on Multimedia, pages 855–858.
Bradfer-Lawrence, T., Duthie, B., Abrahams, C., Adam, M., Barnett, R. J., Beeston, A., Darby, J., Dell, B., Gardner, N., Gasc, A., Heath, B., Howells, N., Janson, M., Kyoseva, M.-V., Luypaert, T., Metcalf, O. C., Nousek-McGregor, A. E., Poznansky, F., Ross, S. R. P.-J., Sethi, S., Smyth, S., Waddell, E., and Froidevaux, J. S. P. (2024). The acoustic index user’s guide: A practical manual for defining, generating and understanding current and future acoustic indices. Methods in Ecology and Evolution.
Buxton, R. T., McKenna, M. F., Clapp, M., Meyer, E., Stabenau, E., Angeloni, L. M., Crooks, K., and Wittemyer, G. (2018). Efficacy of extracting indices from large-scale acoustic recordings to monitor biodiversity. Conservation Biology, 32(5):1174–1184.
Colonna, J. G., Carvalho, J. R. H., and Rosso, O. A. (2020). Estimating ecoacoustic activity in the amazon rainforest through information theory quantifiers. PLOS ONE, 15(7).
Depraetere, M., Pavoine, S., Jiguet, F., Gasc, A., Duvail, S., and Sueur, J. (2012). Monitoring animal diversity using acoustic indices: Implementation in a temperate woodland. Ecological Indicators, 13(1):46–54.
Farina, A. (2018). Perspectives in ecoacoustics: A contribution to defining a discipline. Journal of Ecoacoustics, 2:TRZD5I.
Hill, A. P., Prince, P., Piña Covarrubias, E., Doncaster, C. P., Snaddon, J. L., and Rogers, A. (2018). Audiomoth: Evaluation of a smart open acoustic device for monitoring biodiversity and the environment. Methods in Ecology and Evolution, 9(5):1199–1211.
Kadish, D. and Stoy, K. (2022). Bioacoustic index tool: long-term biodiversity monitoring using on-sensor acoustic index calculations. Bioacoustics, 31(3):348–378.
Kaggle, Xeno-canto, and Contributors, V. (2022). Birdclef 2022 - bird species recognition in soundscape recordings. Kaggle Competition Dataset.
Mennill, D. J. (2024). Field tests of small autonomous recording units: an evaluation of in-person versus automated point counts and a comparison of recording quality. Bioacoustics, 33(2):157–177.
Towsey, M., Wimmer, J., Williamson, I., and Roe, P. (2014). The use of acoustic indices to determine avian species richness in audio-recordings of the environment. Ecological Informatics, 21:110–119. Ecological Acoustics.
Ulloa, J. S., Haupert, S., Latorre, J. F., Aubin, T., and Sueur, J. (2021). scikit-maad: An open-source and modular toolbox for quantitative soundscape analysis in python. Methods in Ecology and Evolution, 12(12):2334–2340.
Publicado
20/07/2025
Como Citar
MENDES, Pedro Lucas Bezerra; STOLL, Rodolfo; COLONNA, Juan G..
EchoLogger: sistema embarcado para cálculo do índice de riqueza acústica no monitoramento ambiental não invasivo. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 17. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 151-160.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2025.9242.
