A capsule network with pixel-based attention and BGRU for sound event detection

Meng, Jiaxiang; Wang, Xingmei; Jin-li, Wang; Teng, Xuyang; Xu, Yuelin

doi:10.1016/j.dsp.2022.103434

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…In industrial applications, the spectrogram has been used for fault detection using vibration signals [64], fault detection in gearboxes based on sound [65], and fault detection in rotary systems using data from various sensors [66]. Verification of bird diversity [67] and the detection of shoots in forests [68] using ambient sound, seismo-acoustic event prediction using vibration signals and ground waves [69], and an AED system [70] are other state-of-the-art applications that use the spectrogram as a feature extraction method.…”

Section: Literature Reviewmentioning

confidence: 99%

A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems

Hajihashemi,

Gharahbagh,

Hajaboutalebi

et al. 2024

Electronics

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Acoustic event detection (AED) systems, combined with video surveillance systems, can enhance urban security and safety by automatically detecting incidents, supporting the smart city concept. AED systems mostly use mel spectrograms as a well-known effective acoustic feature. The spectrogram is a combination of frequency bands. A big challenge is that some of the spectrogram bands may be similar in different events and be useless in AED. Removing useless bands reduces the input feature dimension and is highly desirable. This article proposes a mathematical feature analysis method to identify and eliminate ineffective spectrogram bands and improve AED systems’ efficiency. The proposed approach uses a Student’s t-test to compare frequency bands of the spectrogram from different acoustic events. The similarity between each frequency band among events is calculated using a two-sample t-test, allowing the identification of distinct and similar frequency bands. Removing these bands accelerates the training speed of the used classifier by reducing the number of features, and also enhances the system’s accuracy and efficiency. Based on the obtained results, the proposed method reduces the spectrogram bands by 26.3%. The results showed an average difference of 7.77% in the Jaccard, 4.07% in the Dice, and 5.7% in the Hamming distance between selected bands using train and test datasets. These small values underscore the validity of the obtained results for the test dataset.

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Section: Literature Reviewmentioning

confidence: 99%

A Feature-Reduction Scheme Based on a Two-Sample t-Test to Eliminate Useless Spectrogram Frequency Bands in Acoustic Event Detection Systems

Hajihashemi,

Gharahbagh,

Hajaboutalebi

et al. 2024

Electronics

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“…The approaches usually proposed in this area are based on multiclass classifiers because SED is considered a multiclass classification problem. In the field of feature extraction, the most commonly used features have been Mel-based features such as Log-Mel [5,6], Log-Mel Power Spectrograms (LMS) [7,8] and Mel Frequency Cepstral Coefficients (MFCC) [9][10][11]. In addition to MFCC, features such as linear predictive coding [12], discrete cosine transforms [13,14], wavelet [9,15], Perceptual Linear Prediction (PLP) [16], Linear Prediction Cepstral Coefficients (LPCC) [17], and Line Spectral Frequencies (LSF) [18] have been used in various studies for SED.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Meng et al [5] used a bidirectional gated recurrent unit (BGRU) as an RNN for sound event detection. Politis et al [65] analyzed the classifiers used in Sound Event Localization and Detection in the DCASE 2019 Challenge and concluded that most were CRNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

Hajihashemi,

Alavigharahbagh,

Machado

et al. 2024

Multimed Tools Appl

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The detection of sound events has become increasingly important due to the development of signal processing methods, social media, and the need for automatic labeling methods in applications such as smart cities, navigation, and security systems. For example, in such applications, it is often important to detect sound events at different levels, such as the presence or absence of an event in the segment, or to specify the beginning and end of the sound event and its duration. This study proposes a method to reduce the feature dimensions of a Sound Event Detection (SED) system while maintaining the system’s efficiency. The proposed method, using Empirical Mode Decomposition (EMD), Intrinsic Mode Functions (IMFs), and extraction of locally regulated features from different IMFs of the signal, shows a promising performance relative to the conventional features of SED systems. In addition, the feature dimensions of the proposed method are much smaller than those of conventional methods. To prove the effectiveness of the proposed features in SED tasks, two segment-based approaches for event detection and sound activity detection were implemented using the suggested features, and their effectiveness was confirmed. Simulation results on the URBAN SED dataset showed that the proposed approach reduces the number of input features by more than 99% compared with state-of-the-art methods while maintaining accuracy. According to the obtained results, the proposed method is quite promising.

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