Data augmentation for the classification of North Atlantic right whales upcalls

Padovese, Bruno; Frazão, Fábio; Kirsebom, O. S.; Matwin, Stan

doi:10.1121/10.0004258

Cited by 17 publications

(15 citation statements)

References 16 publications

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“…Interestingly, none of the fish acoustic studies to date (according to our literature search) mentioned using image augmentation to train their classifier; however, image augmentation has been used to increase the training data size and classification performance when labeling other animal calls [75,77,78]. For example, Padovese et al [79] used image augmentation to generate synthetic calls to increase training data size resulting in increased classifier recall and precision for labeling North Atlantic right whale (Eubalaena glacialis) upcalls. Rasmussen and Širović [80] used scaling and translation augmentation to prevent their classifier from overfitting during the training process.…”

Section: Resnet-50 Classifiermentioning

confidence: 99%

Applying Artificial Intelligence Methods to Detect and Classify Fish Calls from the Northern Gulf of Mexico

Waddell

Rasmussen

Širović

2021

JMSE

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Passive acoustic monitoring is a method that is commonly used to collect long-term data on soniferous animal presence and abundance. However, these large datasets require substantial effort for manual analysis; therefore, automatic methods are a more effective way to conduct these analyses and extract points of interest. In this study, an energy detector and subsequent pre-trained neural network were used to detect and classify six fish call types from a long-term dataset collected in the northern Gulf of Mexico. The development of this two-step methodology and its performance are the focus of this paper. The energy detector by itself had a high recall rate (>84%), but very low precision; however, a subsequent neural network was used to classify detected signals and remove noise from the detections. Image augmentation and iterative training were used to optimize classification and compensate for the low number of training images for two call types. The classifier had a relatively high average overall accuracy (>87%), but classifier average recall and precision varied greatly for each fish call type (recall: 39–91%; precision: 26–94%). This coupled methodology expedites call extraction and classification and can be applied to other datasets that have multiple, highly variable calls.

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Section: Resnet-50 Classifiermentioning

confidence: 99%

Applying Artificial Intelligence Methods to Detect and Classify Fish Calls from the Northern Gulf of Mexico

Waddell

Rasmussen

Širović

2021

JMSE

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“…In recent years, the focus has shifted towards spectrogram data augmentation, where data augmentation is applied to the spectrogram features of audio. Techniques in this category include time warping, frequency masking, and time masking [25,26], which have yielded promising results. Building upon this foundation, enhanced spectrogram augmentation methods like Filteraugment [27] and SpecSub [28] have been introduced.…”

Section: Introductionmentioning

confidence: 99%

Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event

Song,

Xiong,

Wang

et al. 2024

Applied Sciences

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Indoor abnormal sound event identification refers to the automatic detection and recognition of abnormal sounds in an indoor environment using computer auditory technology. However, the process of model training usually requires a large amount of high-quality data, which can be time-consuming and costly to collect. Utilizing limited data has become another preferred approach for such research, but it introduces overfitting issues for machine learning models on small datasets. To overcome this issue, we proposed and validated the framework of combining the offline augmentation of raw audio and online augmentation of spectral features, making the application of small datasets in indoor anomalous sound event identification more feasible. Along with this, an improved two-dimensional audio convolutional neural network (EANN) was also proposed to evaluate and compare the impacts of different data augmentation methods under the framework on the sensitivity of sound event identification. Moreover, we further investigated the performance of four combinations of data augmentation techniques. Our research shows that the proposed combined data augmentation method has an accuracy of 97.4% on the test dataset, which is 10.6% higher than the baseline method. This demonstrates the method’s potential in the identification of indoor abnormal sound events.

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“…Unsurprisingly, some traditional music data augmentation methods, such as pitch shifting, time stretching, and adding background noise, have proven effective at this classification task. When synthesizing dolphin calls, care should be taken to apply augmentations to the audio signal rather than to the spectrograms since altering the spectrogram could distort the timefrequency patterns of dolphin whistles, which would result in the semantic integrity of the labels being compromised [29,34]. In [29], primitive shapes were interjected into the audio signal to generate realistic ambient sounds in negative samples, and classical computer vision methods were used to create synthetic time-frequency whistles, which replaced the training data.…”

Section: Introductionmentioning

confidence: 99%

Building Ensemble of Resnet for Dolphin Whistle Detection

Nanni¹,

Cuza²,

Brahnam³

2023

Preprint

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To effectively preserve marine environments and manage endangered species, it is necessary to employ efficient, precise, and scalable solutions for environmental monitoring. Ecoacoustics provides several benefits as it enables non-intrusive, prolonged sampling of environmental sounds, making it a promising tool for conducting biodiversity surveys. However, analyzing and interpreting acoustic data can be time-consuming and often demands substantial human supervision. This challenge can be addressed by harnessing contemporary methods for automated audio signal analysis, which have exhibited remarkable performance due to advancements in deep learning research. This paper introduces a research investigation into developing an automatic computerized system to detect dolphin whistles. The proposed method utilizes a fusion of various resnet50 networks integrated with data augmentation techniques. Through extensive experiments conducted on a publically available benchmark, our findings demonstrate that our ensemble yields significant performance enhancements across all evaluated metrics. The MATLAB/PyTorch source code is freely available at: https://github.com/LorisNanni/

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Data augmentation for the classification of North Atlantic right whales upcalls

Cited by 17 publications

References 16 publications

Applying Artificial Intelligence Methods to Detect and Classify Fish Calls from the Northern Gulf of Mexico

Applying Artificial Intelligence Methods to Detect and Classify Fish Calls from the Northern Gulf of Mexico

Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event

Building Ensemble of Resnet for Dolphin Whistle Detection

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