Research on Scattering Transform of Urban Sound Events Detection Based on Self-Attention Mechanism

Song, Shen; Zhang, Cong; Wei, Zhihui

doi:10.1109/access.2022.3222495

Cited by 3 publications

(6 citation statements)

References 53 publications

(48 reference statements)

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“…Cons: Unable to handle road topology structure information. Self-Attention Mechanism (Song et al, 2022 ): Pros: Weights aggregation of input at different positions, extracting important features. Cons: High computational complexity when dealing with large-scale road networks.…”

Section: Introductionmentioning

confidence: 99%

Design of urban road fault detection system based on artificial neural network and deep learning

Lin

2024

Front. Neurosci.

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IntroductionIn urban traffic management, the timely detection of road faults plays a crucial role in improving traffic efficiency and safety. However, conventional methods often fail to fully leverage the information from road topology and traffic data.MethodsTo address this issue, we propose an innovative detection system that combines Artificial Neural Networks (ANNs), specifically Graph Convolutional Networks (GCN), Bidirectional Gated Recurrent Units (BiGRU), and self-attention mechanisms. Our approach begins by representing the road topology as a graph and utilizing GCN to model it. This allows us to learn the relationships between roads and capture their structural dependencies. By doing so, we can effectively incorporate the spatial information provided by the road network. Next, we employ BiGRU to model the historical traffic data, enabling us to capture the temporal dynamics and patterns in the traffic flow. The BiGRU architecture allows for bidirectional processing, which aids in understanding the traffic conditions based on both past and future information. This temporal modeling enhances our system's ability to handle time-varying traffic patterns. To further enhance the feature representations, we leverage self-attention mechanisms. By combining the hidden states of the BiGRU with self-attention, we can assign importance weights to different temporal features, focusing on the most relevant information. This attention mechanism helps to extract salient features from the traffic data. Subsequently, we merge the features learned by GCN from the road topology and BiGRU from the traffic data. This fusion of spatial and temporal information provides a comprehensive representation of the road status.Results and discussionsBy employing a Multilayer Perceptron (MLP) as a classifier, we can effectively determine whether a road is experiencing a fault. The MLP model is trained using labeled road fault data through supervised learning, optimizing its performance for fault detection. Experimental evaluations of our system demonstrate excellent performance in road fault detection. Compared to traditional methods, our system achieves more accurate fault detection, thereby improving the efficiency of urban traffic management. This is of significant importance for city administrators, as they can promptly identify road faults and take appropriate measures for repair and traffic diversion.

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Section: Introductionmentioning

confidence: 99%

Design of urban road fault detection system based on artificial neural network and deep learning

Lin

2024

Front. Neurosci.

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“…STFT, on the other hand, is limited by the trade-offs involved in time and frequency resolution [ 22 ]. Also, it is not stable against time-warping deformations [ 23 ]. Among the spectral features, the Mel frequency cepstrum (MFC) and the Mel frequency cepstral coefficients (MFCCs) have been used for rare event classification (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…timbral structures such as attacks, frequency and amplitude modulations, and interference in musical chords. The coefficients of the wavelet transform, on the other hand, are calculated over larger window sizes thus allowing these larger structures to be captured without loss of information [ 23 ]. The accuracy of the sound classification task is found to improve with WSC as compared to STFT and MFCC [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

“…The outputs of the two classifiers are combined by using the mathematical formula whose parameters are determined by a genetic algorithm. In the case of environmental sounds, the model of [ 23 ] uses WSN in fusion with the self-attention mechanism for ASC. In the ASC model of [ 23 ], WSN is used to extract features that are processed by the classical feed-forward neural network.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of environmental sounds, the model of [ 23 ] uses WSN in fusion with the self-attention mechanism for ASC. In the ASC model of [ 23 ], WSN is used to extract features that are processed by the classical feed-forward neural network. The gunshot localization and classification model proposed in [ 42 ] uses wavelet MRA for denoising the acoustic signals contaminated by wind noise recorded by four microphones.…”

Section: Introductionmentioning

confidence: 99%

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DEW: A wavelet approach of rare sound event detection

Gul,

Khan,

Ur-Rehman

2024

PLoS ONE

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This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called ‘chunks’ are cropped from the input audio signal corresponding to the estimated locations of the significant transitions. Features from these chunks are extracted by the wavelet scattering network (WSN) and are given as input to a support vector machine (SVM) classifier, which classifies them. The proposed SED framework produces an error rate comparable to the SED systems based on convolutional neural network (CNN) architecture. Also, the proposed algorithm is computationally efficient and lightweight as compared to deep learning models, as it has no learnable parameter. It requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on CNNs and deep neural networks (DNNs), CNN with long short-term memory (LSTM) network, convolutional recurrent neural network (CRNN), and CNN respectively. The proposed model neither requires concatenation with previous frames for anomaly detection nor any additional training data creation needed for other comparative deep learning models. It needs to check almost 360 times fewer chunks for the presence of rare events than the other baseline systems used for comparison in this paper. All these characteristics make the proposed system suitable for real-time applications on resource-limited devices.

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A Lightweight Channel and Time Attention Enhanced 1D CNN Model for Environmental Sound Classification

Xu,

Tian,

Ren

et al. 2024

Expert Systems with Applications

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Research on Scattering Transform of Urban Sound Events Detection Based on Self-Attention Mechanism

Cited by 3 publications

References 53 publications

Design of urban road fault detection system based on artificial neural network and deep learning

Design of urban road fault detection system based on artificial neural network and deep learning

DEW: A wavelet approach of rare sound event detection

A Lightweight Channel and Time Attention Enhanced 1D CNN Model for Environmental Sound Classification

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