Classification of Hydroacoustic Signals Based on Harmonic Wavelets and a Deep Learning Artificial Intelligence System

Kaplun, Dmitry; Voznesensky, A. S.; Romanov, S. A.; Андреев, В. С.; Butusov, Denis N.

doi:10.3390/app10093097

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…DL networks can classify their sound signals to explore the species and population of whales. [139][140][141][142][143] When it is difficult to collect images of some aquatic animals, DL can still be used to classify them by collecting their sound information through sonar and other sensors, which is of positive significance for monitoring a certain species in the deep sea or protecting endangered species.…”

Section: Species Classificationmentioning

confidence: 99%

“…Whales and dolphins are rare marine animals in the world, whose image data is difficult to obtain, but the sound signal of whales can be collected by sonar and other ways. DL networks can classify their sound signals to explore the species and population of whales 139–143 . When it is difficult to collect images of some aquatic animals, DL can still be used to classify them by collecting their sound information through sonar and other sensors, which is of positive significance for monitoring a certain species in the deep sea or protecting endangered species.…”

Section: Applicationsmentioning

confidence: 99%

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Deep learning for visual recognition and detection of aquatic animals: A review

Deng

et al. 2022

Reviews in Aquaculture

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The ocean is an important ecosystem, and aquatic animals play an important role in the biological world, especially in aquaculture. How to accurately and intelligently recognise and detect aquatic animals is one of the urgent problems in the field of underwater biological detection. The wide applications of artificial intelligence (AI), especially deep learning (DL), provide new opportunities and challenges for the efficient and intelligent exploration of aquatic animals. DL has been widely used in the visual recognition and detection of terrestrial animals, but it is in the early stages of use for aquatic animals due to the complexity of underwater environment and the difficulty of data acquisition. Here, this article reviews the current application status of DL for aquatic animals, potential challenges and future directions. The key advances of DL algorithms applied to the visual recognition and detection of aquatic animals are generalised, including datasets, algorithms and performance. The applications of DL are summarised in aquatic animals, including image detection, video detection, species classification, biomass estimation, behaviour analysis and food safety. Furthermore, the challenges are summed up and classified in the object recognition and detection domain for aquatic animals. Finally, further research direction is discussed and the conclusions are drawn. The key advances of DL in the recognition and detection of aquatic animals will help to further excavate and extend the application of DL in the field of marine biological exploration.

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Section: Species Classificationmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Deep learning for visual recognition and detection of aquatic animals: A review

Deng

et al. 2022

Reviews in Aquaculture

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“…The automatic approaches clearly help to better describe these songs. A very large panel of methods were proposed, for example, based on the information entropy (Suzuki et al, 2005), on a threshold on the Fourier coefficients (Mellinger, 2005), on the extraction of the edge contour (Gillespie, 2004), on the analysis of the mel-frequency cepstral coefficient (MFCC) (Pace et al, 2010) or the wavelet coefficients (Kaplun et al, 2020), or with the use of artificial neural networks (Allen et al, 2017;Mohebbi-Kalkhoran, 2019). To go further with the perception of these vocalizations, new representations were suggested and were very interesting to better extract the similarities of units in these songs, especially based on colored pictograms (Rothenberg and Deal, 2015).…”

Section: Temporal Aspects Of Soundsmentioning

confidence: 99%

Bidirectional Interactions With Humpback Whale Singer Using Concrete Sound Elements

et al. 2021

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We describe an art–science project called “Feral Interactions—The Answer of the Humpback Whale” inspired by humpback whale songs and interactions between individuals based on mutual influences, learning process, or ranking in the dominance hierarchy. The aim was to build new sounds that can be used to initiate acoustic interactions with these whales, not in a one-way direction, as playbacks do, but in real interspecies exchanges. Thus, we investigated how the humpback whales generate sounds in order to better understand their abilities and limits. By carefully listening to their emitted vocalizations, we also describe their acoustic features and temporal structure, in a scientific way and also with a musical approach as it is done with musique concrète, in order to specify the types and the morphologies of whale sounds. The idea is to highlight the most precise information to generate our own sounds that will be suggested to the whales. Based on the approach developed in musique concrète, similarities with the sounds produced by bassoon were identified and then were processed to become “concrete sound elements.” This analysis also brought us to design a new music interface that allows us to create adapted musical phrases in real-time. With this approach, interactions will be possible in both directions, from and to whales.

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“…In addition to lung sound analysis, other research has utilized methods such as the wavelet transform and the spectrogram [ 24 ], or empirical mode decomposition (EMD) and bandpass filtering for scale selection, as well as processing continuous wavelet transform (CWT)-based scalogram representations with a lightweight CNN for classification of various respiratory diseases. Recent advancements in noninvasive monitoring have led to significant progress in deriving respiratory signals from ECG data.…”

Section: Introductionmentioning

confidence: 99%

Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning

Khan,

Saeed

et al. 2024

Bioengineering

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Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds.

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Classification of Hydroacoustic Signals Based on Harmonic Wavelets and a Deep Learning Artificial Intelligence System

Cited by 9 publications

References 19 publications

Deep learning for visual recognition and detection of aquatic animals: A review

Deep learning for visual recognition and detection of aquatic animals: A review

Bidirectional Interactions With Humpback Whale Singer Using Concrete Sound Elements

Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning

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