An Efficient Learning Method for Sound Classification using Transfer Learning for Hammering Test

Fukumura, Tsubasa; Aratame, Hayato; Ito, Atsushi; Koike, Masafumi; Hibino, Katsuhiko; Kawamura, Y.

doi:10.1109/sensors47125.2020.9278819

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The hardware was Jetson Nano. As a result, using 2321 training data and 702 test data, the accuracy of the hammering test experiment reached 90.2% [11]. However, this device had some problems.…”

Section: Our Previous Studymentioning

confidence: 99%

“…Recent high-performance smartphones can train NN models in a short time. In addition, in our previous study, we have shown that Transfer Learning is effective for training models in the field with little training data [11]. However, the environment to run Transfer Learning on the smartphone is not ready, so we compared the time to train the NN model on Jetson Nano (EX1-training), the smartphone (EX2-training), and the cloud GPU (EX3-training).…”

Section: Comparison Of Training Time For the Nn Modelmentioning

confidence: 99%

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Improvement of Sound Classification Method on Smartphone for Hammering Test Using 5G Network

Fukumura

Aratame

Ito

et al. 2022

IJNC

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The demand for inspections has increased due to the aging of concrete structures and tile wall surfaces. The hammering test is a simple inspection method, but the inspector needs much experience distinguishing the hammering sounds. Therefore, we developed a device that automatically classifies the hammering sounds using deep learning. However, the hardware with GPU for deep learning is the one-board microcomputer, which has no display, a battery, or an input device, so the inspector cannot change the settings or check the status during the hammering test. Therefore, we used a smartphone instead of a one-board microcomputer. We also used a cloud GPU since a smartphone does not have GPU. The results showed that communication time was the bottleneck. So we considered using a 5G network and compared the classification time, training time, and battery life of the smartphone. As a result, although the training time remained the same, we found that the classification speed was 1.46 times faster than the conventional method, and the smartphone's battery life was sufficient.

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Section: Our Previous Studymentioning

confidence: 99%

Section: Comparison Of Training Time For the Nn Modelmentioning

confidence: 99%

Improvement of Sound Classification Method on Smartphone for Hammering Test Using 5G Network

Fukumura

Aratame

Ito

et al. 2022

IJNC

Self Cite

View full text Add to dashboard Cite

show abstract

Improvement of Sound Classification Method on Smartphone for Hammering Test Using 5G Network

Fukumura

Aratame

Ito

et al. 2021

2021 Ninth International Symposium on Computing and Networking Workshops (CANDARW)

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Deep transfer learning-based bird species classification using mel spectrogram images

Baowaly,

Sarkar,

Walid

et al. 2024

PLoS ONE

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The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet technique was utilized for the investigation. The study adapts the fine-tune model to learn the pertinent patterns from mel spectrogram images specific to this bird species classification task. The fine-tuned EfficientNet model combined with a type of Recurrent Neural Networks (RNNs) namely Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). RNNs are employed to capture the temporal dependencies in audio signals, thereby enhancing bird species classification accuracy. The dataset utilized in this work contains nearly 17,000 bird sound recordings across a diverse range of species. The experiment was conducted with several combinations of EfficientNet and RNNs, and EfficientNet-B7 with GRU surpasses other experimental models with an accuracy of 84.03% and a macro-average precision score of 0.8342.

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An Efficient Learning Method for Sound Classification using Transfer Learning for Hammering Test

Cited by 3 publications

References 6 publications

Improvement of Sound Classification Method on Smartphone for Hammering Test Using 5G Network

Improvement of Sound Classification Method on Smartphone for Hammering Test Using 5G Network

Improvement of Sound Classification Method on Smartphone for Hammering Test Using 5G Network

Deep transfer learning-based bird species classification using mel spectrogram images

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