On the use of VGGish as feature extractor for COVID-19 cough classification

Salamea-Palacios, Christian Raul; Sanchez-Almeida, Tarquino; Calderon-Hinojosa, Xavier; Guana-Moya, Javier; Castaneda-Romero, Paulo; Reina-Travez, Jessica

doi:10.1145/3589883.3589896

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…The VGGish model operates on log-mel spectrograms and uses 2D convolutional, pooling, and fully connected layers to extract relevant features from signal spectrograms (Figure 6). VGGIsh has been used very recently as a feature extractor for COVID-19 cough classification [43]. The pre-trained VGGish model was used and fine-tuned by letting it iterate on our dataset.…”

Section: Mixed Rnn-cnn Approach: Vggishmentioning

confidence: 99%

Cough Detection Using Acceleration Signals and Deep Learning Techniques

Sanchez-Morillo,

Sales-Lerida,

Priego-Torres

et al. 2024

Electronics

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Cough is a frequent symptom in many common respiratory diseases and is considered a predictor of early exacerbation or even disease progression. Continuous cough monitoring offers valuable insights into treatment effectiveness, aiding healthcare providers in timely intervention to prevent exacerbations and hospitalizations. Objective cough monitoring methods have emerged as superior alternatives to subjective methods like questionnaires. In recent years, cough has been monitored using wearable devices equipped with microphones. However, the discrimination of cough sounds from background noise has been shown a particular challenge. This study aimed to demonstrate the effectiveness of single-axis acceleration signals combined with state-of-the-art deep learning (DL) algorithms to distinguish intentional coughing from sounds like speech, laugh, or throat noises. Various DL methods (recurrent, convolutional, and deep convolutional neural networks) combined with one- and two-dimensional time and time–frequency representations, such as the signal envelope, kurtogram, wavelet scalogram, mel, Bark, and the equivalent rectangular bandwidth spectrum (ERB) spectrograms, were employed to identify the most effective approach. The optimal strategy, which involved the SqueezeNet model in conjunction with wavelet scalograms, yielded an accuracy and precision of 92.21% and 95.59%, respectively. The proposed method demonstrated its potential for cough monitoring. Future research will focus on validating the system in spontaneous coughing of subjects with respiratory diseases under natural ambulatory conditions.

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Section: Mixed Rnn-cnn Approach: Vggishmentioning

confidence: 99%

Cough Detection Using Acceleration Signals and Deep Learning Techniques

Sanchez-Morillo,

Sales-Lerida,

Priego-Torres

et al. 2024

Electronics

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The Sound of Surveillance: Enhancing Machine Learning-Driven Drone Detection with Advanced Acoustic Augmentation

Kümmritz

2024

Drones

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In response to the growing challenges in drone security and airspace management, this study introduces an advanced drone classifier, capable of detecting and categorizing Unmanned Aerial Vehicles (UAVs) based on acoustic signatures. Utilizing a comprehensive database of drone sounds across EU-defined classes (C0 to C3), this research leverages machine learning (ML) techniques for effective UAV identification. The study primarily focuses on the impact of data augmentation methods—pitch shifting, time delays, harmonic distortion, and ambient noise integration—on classifier performance. These techniques aim to mimic real-world acoustic variations, thus enhancing the classifier’s robustness and practical applicability. Results indicate that moderate levels of augmentation significantly improve classification accuracy. However, excessive application of these methods can negatively affect performance. The study concludes that sophisticated acoustic data augmentation can substantially enhance ML-driven drone detection, providing a versatile and efficient tool for managing drone-related security risks. This research contributes to UAV detection technology, presenting a model that not only identifies but also categorizes drones, underscoring its potential for diverse operational environments.

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On the use of VGGish as feature extractor for COVID-19 cough classification

Cited by 2 publications

References 7 publications

Cough Detection Using Acceleration Signals and Deep Learning Techniques

Cough Detection Using Acceleration Signals and Deep Learning Techniques

The Sound of Surveillance: Enhancing Machine Learning-Driven Drone Detection with Advanced Acoustic Augmentation

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