A real-time music detection method based on convolutional neural network using Mel-spectrogram and spectral flux

2021 ◽  
Vol 263 (1) ◽  
pp. 5910-5918
Author(s):  
Yiya Hao ◽  
Yaobin Chen ◽  
Weiwei Zhang ◽  
Gong Chen ◽  
Liang Ruan
Keyword(s):  
Neural Network ◽  
Online Education ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Speech Intelligibility ◽  
Classical Music ◽  
Detection Method ◽  
Automatic Gain Control ◽  
Real Time Processing ◽  
Spectral Flux

Audio processing, including speech enhancement system, improves speech intelligibility and quality in real-time communication (RTC) such as online meetings and online education. However, such processing, primarily noise suppression and automatic gain control, is harmful to music quality when the captured signal is music instead of speech. A music detector can solve the issue above by switching off the speech processing when the music is detected. In RTC scenarios, the music detector should be low-complexity and cover various situations, including different types of music, background noises, and other acoustical environments. In this paper, a real-time music detection method with low-computation complexity is proposed, based on a convolutional neural network (CNN) using Mel-spectrogram and spectral flux as input features. The proposed method achieves overall 90.63% accuracy under different music types (classical music, instruments solos, singing-songs, etc.), speech languages (English and Mandarin), and noise types. The proposed method is constructed on a lightweight CNN model with a small feature size, which guarantees real-time processing.

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

scholarly journals Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network

2020 ◽  
Vol 10 (14) ◽  
pp. 4720 ◽  
Author(s):  
Zhiqiang Teng ◽  
Shuai Teng ◽  
Jiqiao Zhang ◽  
Gongfa Chen ◽  
Fangsen Cui
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Structural State ◽  
Detection Method ◽  
Steel Frame ◽  
Engineering Practice ◽  
Detection Accuracy ◽  
Vibration Signals

The traditional methods of structural health monitoring (SHM) have obvious disadvantages such as being time-consuming, laborious and non-synchronizing, and so on. This paper presents a novel and efficient approach to detect structural damages from real-time vibration signals via a convolutional neural network (CNN). As vibration signals (acceleration) reflect the structural response to the changes of the structural state, hence, a CNN, as a classifier, can map vibration signals to the structural state and detect structural damages. As it is difficult to obtain enough damage samples in practical engineering, finite element analysis (FEA) provides an alternative solution to this problem. In this paper, training samples for the CNN are obtained using FEA of a steel frame, and the effectiveness of the proposed detection method is evaluated by inputting the experimental data into the CNN. The results indicate that, the detection accuracy of the CNN trained using FEA data reaches 94% for damages introduced in the numerical model and 90% for damages in the real steel frame. It is demonstrated that the CNN has an ideal detection effect for both single damage and multiple damages. The combination of FEA and experimental data provides enough training and testing samples for the CNN, which improves the practicability of the CNN-based detection method in engineering practice.

A novel real-time fall detection method based on head segmentation and convolutional neural network

2020 ◽  
Vol 17 (6) ◽  
pp. 1939-1949 ◽  
Author(s):  
Chenguang Yao ◽  
Jun Hu ◽  
Weidong Min ◽  
Zhifeng Deng ◽  
Song Zou ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Fall Detection ◽  
Head Segmentation ◽  
Time Fall

scholarly journals Identification of line status changes using phasor measurements in transient states through deep learning networks

2020 ◽  
Vol 216 ◽  
pp. 01035
Author(s):  
Natalja Gotman ◽  
Galina Shumilova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Electrical Network ◽  
Learning Networks ◽  
Transient States ◽  
Phasor Measurements ◽  
State Detection

The problem of detecting changes in a topology of an electrical network in real time is solved. This paper proposes a line state detection method based on a convolutional neural network (CNN) classifier using phasor measurements of bus voltages and currents in transient states.

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