This work introduces the one-shot learning paradigm in the computational bioacoustics domain. Even though, most of the related literature assumes availability of data characterizing the entire class dictionary of the problem at hand, that is rarely true as a habitat’s species composition is only known up to a certain extent. Thus, the problem needs to be addressed by methodologies able to cope with non-stationarity. To this end, we propose a framework able to detect changes in the class dictionary and incorporate new classes on the fly. We design an one-shot learning architecture composed of a Siamese Neural Network operating in the logMel spectrogram space. We extensively examine the proposed approach on two datasets of various bird species using suitable figures of merit. Interestingly, such a learning scheme exhibits state of the art performance, while taking into account extreme non-stationarity cases.

One-shot learning for acoustic identification of bird species in non-stationary environments / M. Acconcjaioco, S. Ntalampiras (INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION). - In: 2020 25th International Conference on Pattern Recognition (ICPR)[s.l] : IEEE, 2021. - ISBN 9781728188089. - pp. 755-762 (( convegno ICPR tenutosi a Milano nel 2021 [10.1109/ICPR48806.2021.9412005].

One-shot learning for acoustic identification of bird species in non-stationary environments

S. Ntalampiras
2021

Abstract

This work introduces the one-shot learning paradigm in the computational bioacoustics domain. Even though, most of the related literature assumes availability of data characterizing the entire class dictionary of the problem at hand, that is rarely true as a habitat’s species composition is only known up to a certain extent. Thus, the problem needs to be addressed by methodologies able to cope with non-stationarity. To this end, we propose a framework able to detect changes in the class dictionary and incorporate new classes on the fly. We design an one-shot learning architecture composed of a Siamese Neural Network operating in the logMel spectrogram space. We extensively examine the proposed approach on two datasets of various bird species using suitable figures of merit. Interestingly, such a learning scheme exhibits state of the art performance, while taking into account extreme non-stationarity cases.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/842101
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