Porn streamer audio recognition based on deep learning and random Forest

Liu, Shangfeng; Li, Ruwei; Li, Qiuyan; Zhao, Jingyu

doi:10.1007/s10489-023-04491-x

Cited by 2 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feature extraction is a critical step in classification and recognition using deep learning algorithms. The Mel filter bank, Gammatone filter bank and Bark filter bank are used to extract the spectrograms of sound signals in speech recognition and classification studies based on sound signals [24][25][26][27]. In order to extract richer features, a method for extracting fusion spectrograms is proposed, as shown in Figure 2.…”

Section: Arc Sound Feature Extractionmentioning

confidence: 99%

Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network

Yang,

Guan,

Yang

et al. 2023

Electronics

View full text Add to dashboard Cite

The CMT welding process has been widely used for aluminum alloy welding. The weld’s penetration state is essential for evaluating the welding quality. Arc sound signals contain a wealth of information related to the penetration state of the weld. This paper studies the correlation between the frequency domain features of arc sound signals and the weld penetration state, as well as the correlation between Mel spectrograms, Gammatone spectrograms and Bark spectrograms and the weld penetration state. Arc sound features fused with multilingual spectrograms are constructed as inputs to a custom Inception CNN model that is optimized based on GoogleNet for CMT weld penetration state recognition. The experimental results show that the accuracy of the method proposed in this paper for identifying the fusion state of CMT welds in aluminum alloy plates is 97.7%, which is higher than the identification accuracy of a single spectrogram as the input. The recognition accuracy of the customized Inception CNN is improved by 0.93% over the recognition accuracy of GoogleNet. The customized Inception CNN also has high recognition results compared to AlexNet and ResNet.

show abstract

Section: Arc Sound Feature Extractionmentioning

confidence: 99%

Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network

Yang,

Guan,

Yang

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…However, due to the limitation of small samples, the application of deep learning in the field of thermal image classification and recognition of power equipment is still relatively limited. In recent years, the emergence of transfer learning has effectively addressed the issue of small sample sizes, bringing promising prospects for the application of deep learning in the field of thermal image classification and recognition of power equipment [7][8].…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network-based edge detection technique for thermal images of electrical equipment

Tian,

Liu,

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

View full text Add to dashboard Cite

Infrared detection is a common means of inspecting current electrical equipment. It has the advantages of not requiring power outage, non-contact, non-disassembly, long-distance, large-area rapid scanning imaging, safety, reliability, accuracy, and efficiency. However, infrared images of electrical equipment often suffer from low image resolution and high background noise, which poses a certain challenge to the detection of electrical equipment. Therefore, this paper proposes an edge detection technique based on convolutional neural networks. Firstly, the NL-means algorithm is used to denoise the obtained infrared images of electrical equipment. Then, grayscale histogram equalization is applied to enhance the images. The preprocessed images are input into the convolutional neural network to obtain the final edge detection image. Experiments show that the algorithm proposed in this paper can effectively segment electrical equipment from the infrared detection images.

show abstract

Porn streamer audio recognition based on deep learning and random Forest

Cited by 2 publications

References 25 publications

Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network

Penetration State Identification of Aluminum Alloy Cold Metal Transfer Based on Arc Sound Signals Using Multi-Spectrogram Fusion Inception Convolutional Neural Network

Convolutional neural network-based edge detection technique for thermal images of electrical equipment

Contact Info

Product

Resources

About