Uncertainty clustering internal validity assessment using Fréchet distance for unsupervised learning

Rendon, Nestor; Giraldo, Jhony H.; Bouwmans, Thierry; Rodríguez‐Buriticá, Susana; Ramı́rez, Erika; Isaza, Claudia

doi:10.1016/j.engappai.2023.106635

Cited by 5 publications

(1 citation statement)

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“…Gibb et al [ 15 ] discuss the limitations of supervision in soundscape analysis and propose variational autoencoders to embed latent features from acoustic survey data and evaluate habitat degradation. Rendon et al [ 23 ] proposed the Uncertainty Fréchet Clustering Internal Validity Index, which was assessed using real-world and synthetic data, including a soundscape dataset identifying the transformation of ecosystems. On the other hand, Allaoi et al [ 24 ] investigated the problem of treating embedding and clustering simultaneously to uncover data structure reliably by constraining manifold embedding through clustering and introduce the UEC method.…”

Section: Related Workmentioning

confidence: 99%

Soundscape Characterization Using Autoencoders and Unsupervised Learning

Nieto-Mora,

Ferreira de Oliveira,

Sanchez-Giraldo

et al. 2024

Sensors

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Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, acoustic diversity, community interactions, and human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due to the large volumes of ARU data. In this work, we propose a non-supervised framework using autoencoders to extract soundscape features. We applied this framework to a dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based on autoencoder features and represents cluster information with prototype spectrograms using centroid features and the decoder part of the neural network. Our analysis provides valuable insights into the distribution and temporal patterns of various sound compositions within the study area. By utilizing autoencoders, we identify significant soundscape patterns characterized by recurring and intense sound types across multiple frequency ranges. This comprehensive understanding of the study area’s soundscape allows us to pinpoint crucial sound sources and gain deeper insights into its acoustic environment. Our results encourage further exploration of unsupervised algorithms in soundscape analysis as a promising alternative path for understanding and monitoring environmental changes.

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Section: Related Workmentioning

confidence: 99%