Learning Attentive Representations for Environmental Sound Classification

Zhang, Zhichao; Xu, Shugong; Zhang, Shunqing; Qiao, Tianhao; Cao, Shan

doi:10.1109/access.2019.2939495

Cited by 61 publications

(48 citation statements)

References 46 publications

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“…The advantage of this method is that the process to manually extract features is cancelled. However, 1D-CNN extracts features at the global level without considering the temporal structure and frequency feature of environmental sounds [3]. In second category method, the network is trained by features extracted from raw signal, such as spectrogram and MFCC.…”

Section: Related Workmentioning

confidence: 99%

“…As one of the branches, the accuracy of speech classification and music classification has reached a considerable level, even exceeding the ability of human auditory perception [1], [2]. However, as another branch of speech recognition, environmental sound classification (ESC) still faces many difficulties in various aspects, such as nonstationary nature of environment sound and the strong interference of ambient noise [3]. On the other side, ESC research has an effect on the construction of smart cities [4].…”

Section: Introductionmentioning

confidence: 99%

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Environment Sound Event Classification With a Two-Stream Convolutional Neural Network

et al. 2020

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In recent years, with the construction of intelligent cities, the importance of environmental sound classification (ESC) research has become increasingly prominent. However, due to the non-stationary nature of environment sound and the strong interference of ambient noise, the recognition accuracy of ESC is not high enough. Even with deep learning methods, it is difficult to fully extract features from models with a single input. Aiming to improve the recognition accuracy of ESC, this paper proposes a two-stream convolutional neural network (CNN) based on raw audio CNN (RACNN) and logmel CNN (LMCNN). In this method, a pre-emphasis module is first constructed to deal with raw audio signal. The processed audio data and logmel data are imported into RACNN and LMCNN, respectively to obtain both of time and frequency features of audio. In addition, a random-padding method is proposed to patch shorter data sequences. In such a way, the available data for experiment are greatly increased. Finally, the effectiveness of the methods has been verified based on UrbanSound8K dataset in experimental part. INDEX TERMS Environmental sound classification, sound recognition, convolutional neural networks, data processing, pre-emphasis, two stream model.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Environment Sound Event Classification With a Two-Stream Convolutional Neural Network

et al. 2020

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“…Li et al [17] proposed a multi-stream network with temporal attention in which the structure is composed of three streams, each containing a single temporal attention vector. Zhang et al [9] integrated temporal attention into its CRNN architecture and the same authors [18] proposed a model that combines channel attention and temporal attention together.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Channel Temporal Attention Convolutional Neural Network Model for Environmental Sound Classification

Wang

Feng

Anderson

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Recently, many attention-based deep neural networks have emerged and achieved state-of-the-art performance in environmental sound classification. The essence of attention mechanism is assigning contribution weights on different parts of features, namely channels, spectral or spatial contents, and temporal frames. In this paper, we propose an effective convolutional neural network structure with a multichannel temporal attention (MCTA) block, which applies a temporal attention mechanism within each channel of the embedded features to extract channel-wise relevant temporal information. This multi-channel temporal attention structure will result in a distinct attention vector for each channel, which enables the network to fully exploit the relevant temporal information in different channels. The datasets used to test our model include ESC-50 and its subset ESC-10, along with development sets of DCASE 2018 and 2019. In our experiments, MCTA performed better than the single-channel temporal attention model and the non-attention model with the same number of parameters. Furthermore, we compared our model with some successful attention-based models and obtained competitive results with a relatively lighter network.

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“…To manage the obstacles, we considered the role of the attention mechanism. As a novel intelligent method, the attention mechanism, which has the capability to adaptively capture temporal correlations between different sequences [23] and allows for feature extraction networks to focus on the relevant characteristics without signal processing technology and feature engineering, are commonly explored in various structural prediction tasks, such as document classification [24], speech recognition [25][26][27], and environmental classification [28,29]. Therefore, in this paper, we propose a novel ABD method for gear fault diagnosis under different working conditions based on a multi-scale convolutional learning structure and attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions

Yao

Zhang

Yang

et al. 2020

Sensors

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The gear fault signal under different working conditions is non-linear and non-stationary, which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault diagnosis under different working conditions is mainly based on vibration signals. However, vibration signal acquisition is limited by its requirement for contact measurement, while vibration signal analysis methods relies heavily on diagnostic expertise and prior knowledge of signal processing technology. To solve this problem, a novel acoustic-based diagnosis (ABD) method for gear fault diagnosis under different working conditions based on a multi-scale convolutional learning structure and attention mechanism is proposed in this paper. The multi-scale convolutional learning structure was designed to automatically mine multiple scale features using different filter banks from raw acoustic signals. Subsequently, the novel attention mechanism, which was based on a multi-scale convolutional learning structure, was established to adaptively allow the multi-scale network to focus on relevant fault pattern information under different working conditions. Finally, a stacked convolutional neural network (CNN) model was proposed to detect the fault mode of gears. The experimental results show that our method achieved much better performance in acoustic based gear fault diagnosis under different working conditions compared with a standard CNN model (without an attention mechanism), an end-to-end CNN model based on time and frequency domain signals, and other traditional fault diagnosis methods involving feature engineering.

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Learning Attentive Representations for Environmental Sound Classification

Cited by 61 publications

References 46 publications

Environment Sound Event Classification With a Two-Stream Convolutional Neural Network

Environment Sound Event Classification With a Two-Stream Convolutional Neural Network

A Multi-Channel Temporal Attention Convolutional Neural Network Model for Environmental Sound Classification

Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions

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