Bioacoustic Classification of Antillean Manatee Vocalization Spectrograms Using Deep Convolutional Neural Networks

Merchan, Fernando; Guerra, Ariel; Poveda, Héctor; Guzmán, Héctor M.; Sanchez-Galán, Javier E.

doi:10.3390/app10093286

Cited by 17 publications

(12 citation statements)

References 44 publications

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“…The paper from Merchan et al [2] deals with the classification of vocalization spectrograms of Antillean Manatee, a large endangered sea mammal. In order to estimate its population for scientific purposes, the authors turn to audio classification.…”

Section: Deep Learning For Applications In Acousticsmentioning

confidence: 99%

Special Issue on Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening

2021

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Section: Deep Learning For Applications In Acousticsmentioning

confidence: 99%

Special Issue on Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening

2021

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“…Investigations have already been developed to handle tasks such as infant crying motivation [22], music genre classification [23], and music mood classification [24]. The visual domain has also been used with animal vocalizations, in tasks such as species identification and detection [13,25].…”

Section: Signalmentioning

confidence: 99%

On the Importance of Passive Acoustic Monitoring Filters

Aguiar

Maguolo

Nanni

et al. 2021

JMSE

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Passive acoustic monitoring (PAM) is a noninvasive technique to supervise wildlife. Acoustic surveillance is preferable in some situations such as in the case of marine mammals, when the animals spend most of their time underwater, making it hard to obtain their images. Machine learning is very useful for PAM, for example to identify species based on audio recordings. However, some care should be taken to evaluate the capability of a system. We defined PAM filters as the creation of the experimental protocols according to the dates and locations of the recordings, aiming to avoid the use of the same individuals, noise patterns, and recording devices in both the training and test sets. It is important to remark that the filters proposed here were not intended to improve the accuracy rates. Indeed, these filters tended to make it harder to obtain better rates, but at the same time, they tended to provide more reliable results. In our experiments, a random division of a database presented accuracies much higher than accuracies obtained with protocols generated with PAM filters, which indicates that the classification system learned other components presented in the audio. Although we used the animal vocalizations, in our method, we converted the audio into spectrogram images, and after that, we described the images using the texture. These are well-known techniques for audio classification, and they have already been used for species classification. Furthermore, we performed statistical tests to demonstrate the significant difference between the accuracies generated with and without PAM filters with several well-known classifiers. The configuration of our experimental protocols and the database were made available online.

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“…all their samples recorded in only two locations (see Table 1), increasing the number of developed to handle tasks such as infant cry motivation [7], music genre classification 165 [8] and music mood classification [9]. The visual domain has also been used with animal 166 vocalizations, in tasks as species identification and detection [4,10]. 191…”

Section: Introduction 18mentioning

confidence: 99%

On the Importance of Passive Acoustic Monitoring Filters

Aguiar¹,

Maguolo²,

Nanni³

et al. 2021

Preprint

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Passive acoustic monitoring (PAM) is a non-invasive technique to supervise the wildlife. Acoustic surveillance is preferable in some situation such as in the case of marine mammals, when the animals spend most of their time underwater, making it hard to obtain their images. Machine learning is very useful for PAM, for example, to identify species based on audio recordings. But some care should be taken to evaluate the capability of a system. We deﬁne PAM-ﬁlters as the creation of the experimental protocols according to the dates and locations of the recordings, aiming to avoid the use of the same individuals, noise and recording devices in both training and test sets. A random division of a database present accuracies much higher than accuracies obtained with protocols generated with PAM-ﬁlter. Although we use the animal vocalizations, in our method we convert the audio into spectrogram images, after that, we describe the images using the texture. Those are well-known techniques for audio classiﬁcation, and they have already been used for species classiﬁcation. Also, we perform statistical tests to demonstrate the signiﬁcant difference between accuracies generated with and without PAM-ﬁlters with several well-known classiﬁers. The conﬁguration of our experimental protocols and the database were made available online.

show abstract

Bioacoustic Classification of Antillean Manatee Vocalization Spectrograms Using Deep Convolutional Neural Networks

Cited by 17 publications

References 44 publications

Special Issue on Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening

Special Issue on Deep Learning for Applications in Acoustics: Modeling, Synthesis, and Listening

On the Importance of Passive Acoustic Monitoring Filters

On the Importance of Passive Acoustic Monitoring Filters

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