Convolutional Neural Network-Gated Recurrent Unit Neural Network with Feature Fusion for Environmental Sound Classification

Zeng, Jinfang; Li, Youming; Chen, Da

doi:10.3103/s0146411621040106

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…Convolutional neural network Recall: 95% [17] 2023 UrbanSound8K Hybrid ensemble classifier Accuracy: 79% [18] 2020 ESC-10 sound signals data. Support Vector Machines Accuracy: 94% [19] 2021 UrbanSound8K Convolutional neural network Accuracy: 96% convolutional layers that capture signals and learn relevant filters for classification. Hyperparameters selection and model evaluation was carried out using Bayesian optimization with cross-validation.…”

Section: Refmentioning

confidence: 99%

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Preventing Crimes Through Gunshots Recognition Using Novel Feature Engineering and Meta-Learning Approach

Raza,

Rustam,

Mallampati

et al. 2023

IEEE Access

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Gunshot sounds are common in crimes, particularly those involving threats, harassment, or killing. The gunshot sounds in crimes can create fear and panic among victims, often leading to psychological trauma. Gunshot sounds are associated with a significant mortality rate, especially in cases of gun violence. The sound of gunshots can serve as evidence in criminal investigations, allowing law enforcement officials to determine the number of shots fired, the caliber of the gun used, and the distance from which the shots were fired. Efficient gunshot detection is necessary to address the issue of gun violence in society. This study aims to detect gunshot sounds using an efficient approach to prevent crimes. The frequency-time domain spectrum analysis is performed to understand the patterns of signals related to each target class. A novel Discrete Wavelet Transform Random Forest Probabilistic (DWT-RFP) feature engineering approach is proposed, which takes Mel-frequency cepstral coefficients (MFCC) extracted from gunshot sound data as input for feature extraction. A novel meta-learning-based Meta-RF-KN (MRK) is proposed to detect gunshots based on newly created ensemble features with a DWT-RFP approach. For experiments, the gunshot sounds dataset containing 851 audio clips collected from public videos on YouTube from eight kinds of gun models, is used. Advanced machine learning and deep learning techniques are applied in comparison to evaluate the performance of the proposed approach. Extensive experiments show that the proposed MRK approach achieves 99% k-fold accuracy for detecting gunshots and outperforms state-of-the-art approaches. The proposed approach can potentially be used for accurate gunshot detection and to help prevent crimes.

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Section: Refmentioning

confidence: 99%

“…The study [19] introduced a novel technique, called LMCC, to enhance environmental sound classification which fuses Log mel, log-scaled cochleagram, and log-scaled constant-Q transform features. The LMCC features are then fed into the CNN-GRUNN, a network comprising both a convolutional neural network and a gated recurrent unit neural network.…”

Section: Refmentioning

confidence: 99%

Preventing Crimes Through Gunshots Recognition Using Novel Feature Engineering and Meta-Learning Approach

Raza,

Rustam,

Mallampati

et al. 2023

IEEE Access

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Integration of multi-point influence line information for damage localization of bridge structures

Zhang,

Zhu,

Zhou

2023

J Civil Struct Health Monit

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Training environmental sound classification models for real-world deployment in edge devices

Goulão,

Bandeira,

Martins

et al. 2024

Discov Appl Sci

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The interest in smart city technologies has grown in recent years, and a major challenge is to develop methods that can extract useful information from data collected by sensors in the city. One possible scenario is the use of sound sensors to detect passing vehicles, sirens, and other sounds on the streets. However, classifying sounds in a street environment is a complex task due to various factors that can affect sound quality, such as weather, traffic volume, and microphone quality. This paper presents a deep learning model for multi-label sound classification that can be deployed in the real world on edge devices. We describe two key components, namely data collection and preparation, and the methodology to train the model including a pre-train using knowledge distillation. We benchmark our models on the ESC-50 dataset and show an accuracy of 85.4%, comparable to similar state-of-the-art models requiring significantly more computational resources. We also evaluated the model using data collected in the real world by early prototypes of luminaires integrating edge devices, with results showing that the approach works well for most vehicles but has significant limitations for the classes “person” and “bicycle”. Given the difference between the benchmarking and the real-world results, we claim that the quality and quantity of public and private data for this type of task is the main limitation. Finally, all results show great benefits in pretraining the model using knowledge distillation.

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Convolutional Neural Network-Gated Recurrent Unit Neural Network with Feature Fusion for Environmental Sound Classification

Cited by 8 publications

References 30 publications

Preventing Crimes Through Gunshots Recognition Using Novel Feature Engineering and Meta-Learning Approach

Preventing Crimes Through Gunshots Recognition Using Novel Feature Engineering and Meta-Learning Approach

Integration of multi-point influence line information for damage localization of bridge structures

Training environmental sound classification models for real-world deployment in edge devices

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