Acoustic scene classification based on Mel spectrogram decomposition and model merging

Zhang, Tao; Guo, Feng; Liang, Jinhua; An, Tong

doi:10.1016/j.apacoust.2021.108258

Cited by 40 publications

(14 citation statements)

References 8 publications

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“…The mel spectrogram [ 36 ] is a combination of the mel scale and a spectrogram. The acquisition process is simple; the input signal is preprocessed (framing, windowing), fast Fourier transform (FFT) is applied and the signal is passed through a mel filter bank.…”

Section: Methodologiesmentioning

confidence: 99%

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

Yang

Peng

Zhou³

et al. 2023

Micromachines

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Refractory materials are basic materials widely used in industrial furnaces and thermal equipment. Their microstructure is similar to that of many heterogeneous high-performance materials used in micro/nanodevices. The presence of damage can reduce the mechanical properties and service life of refractory materials and even cause serious safety accidents. In this paper, a novel percussion and particle swarm optimization-support vector machine (PSO-SVM)-based method is proposed to detect damage in refractory materials. An impact is applied to the material and the generated sound is recorded. The percussion-induced sound signals are fed into a mel filter bank to generate time–frequency representations in the form of mel spectrograms. Then, two image descriptors—the local binary pattern (LBP) and histogram of oriented gradient (HOG)—are used to extract the texture information of the mel spectrogram. Finally, combining both HOG and LBP features, the fused features are input to the PSO-SVM algorithm to realize damage detection in refractory materials. The results demonstrated that the proposed method could identify five different degrees of damage of refractory materials, with an accuracy rate greater than 97%. Therefore, the percussion and PSO-SVM-based method proposed in this paper has high potential for field applications in damage detection in refractory material, and also has the potential to be extended to research on damage detection methods for other materials used in micro/nanodevices.

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Section: Methodologiesmentioning

confidence: 99%

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

Yang

Peng

Zhou³

et al. 2023

Micromachines

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“…A Mel spectrogram translates acoustic signals into a visual representation that depicts sound intensity in Mel-frequency bands over time, and it is widely used in audio data processing. In essence, in other words, with the Mel spectrogram transforms applied, SED is shifted from a problem of distinguishing the inherent acoustic characteristics in sound to a challenge of distinguishing the visual features of the Mel spectrogram [ 16 , 17 , 18 ].…”

Section: Methodsmentioning

confidence: 99%

Sound-Event Detection of Water-Usage Activities Using Transfer Learning

Hyun

2023

Sensors

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In this paper, a sound event detection method is proposed for estimating three types of bathroom activities—showering, flushing, and faucet usage—based on the sounds of water usage in the bathroom. The proposed approach has a two-stage structure. First, the general sound classification network YAMNet is utilized to determine the existence of a general water sound; if the input data contains water sounds, W-YAMNet, a modified network of YAMNet, is then triggered to identify the specific activity. W-YAMNet is designed to accommodate the acoustic characteristics of each bathroom. In training W-YAMNet, the transfer learning method is applied to utilize the advantages of YAMNet and to address its limitations. Various parameters, including the length of the audio clip, were experimentally analyzed to identify trends and suitable values. The proposed method is implemented in a Raspberry-Pi-based edge computer to ensure privacy protection. Applying this methodology to 10-min segments of continuous audio data yielded promising results. However, the accuracy could still be further enhanced, and the potential for utilizing the data obtained through this approach in assessing the health and safety of elderly individuals living alone remains a topic for future investigation.

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“…The model obtained an accuracy of 87.88 %, and the classification results for COVID-19 and pneumonia were visually confirmed using Grad-CAM. Zhang et al [34] converted the voice data of the 2019 TAU Urban Acoustic Scenes into a Mel spectrogram, classified the voice using ResNet20, and applied Grad-CAM to interpret the results. CAM was applied and analyzed to the Mel spectrogram and MFCC, which converts 1D-based sound into a 2D image.…”

Section: Related Workmentioning

confidence: 99%