A Multipitch Tracking Algorithm Based on Wavelet Packet Analysis

Liting, Chen; Hu, Wenjing; Mo, Jialing; Zhang, Yixuan

doi:10.1088/1742-6596/1437/1/012099

Cited by 1 publication

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fourier transform can only separately analyze data from the time domain or the frequency domain, and cannot combine the two domains to observe the signal. Wavelet transform analyzes the signal via a window function called the wavelet function, which is the same as the short-time Fourier transform [31]. However, a significant peculiarity of the wavelet transform is that the local characteristics of the signal could be analyzed together with both the time and frequency domains.…”

Section: Wavelet Packet Analysismentioning

confidence: 99%

Research on a Cognitive Distraction Recognition Model for Intelligent Driving Systems Based on Real Vehicle Experiments

Sun

Wang

Guo

et al. 2020

Sensors

View full text Add to dashboard Cite

The accurate and prompt recognition of a driver’s cognitive distraction state is of great significance to intelligent driving systems (IDSs) and human-autonomous collaboration systems (HACSs). Once the driver’s distraction status has been accurately identified, the IDS or HACS can actively intervene or take control of the vehicle, thereby avoiding the safety hazards caused by distracted driving. However, few studies have considered the time–frequency characteristics of the driving behavior and vehicle status during distracted driving for the establishment of a recognition model. This study seeks to exploit a recognition model of cognitive distraction driving according to the time–frequency analysis of the characteristic parameters. Therefore, an on-road experiment was implemented to measure the relative parameters under both normal and distracted driving via a test vehicle equipped with multiple sensors. Wavelet packet analysis was used to extract the time–frequency characteristics, and 21 pivotal features were determined as the input of the training model. Finally, a bidirectional long short-term memory network (Bi-LSTM) combined with an attention mechanism (Atten-BiLSTM) was proposed and trained. The results indicate that, compared with the support vector machine (SVM) model and the long short-term memory network (LSTM) model, the proposed model achieved the highest recognition accuracy (90.64%) for cognitive distraction under the time window setting of 5 s. The determination of time–frequency characteristic parameters and the more accurate recognition of cognitive distraction driving achieved in this work provide a foundation for human-centered intelligent vehicles.

show abstract

Section: Wavelet Packet Analysismentioning

confidence: 99%