Time-domain signal reconstruction of vehicle interior noise based on deep learning and compressed sensing techniques. (May 2020) - Wang, X.L.; Yang, D.P.; Wang, Y.S.; Guo, H.; Liu, N.N.; Li, W.W.
Highlights: A novel a Multivariable-based vehicle interior noise time-domain signal reconstruction (MTSR) algorithm is proposed. The SCS method adaptively extracts effective features of noise signals. A new DBN-NN model is established for reconstructing the noise signal of passenger ear-sides. The RCS method is used to recover the time-domain signal. The proposed reconstruction method can realise the rapid signal reconstruction of passenger ear-sides. Abstract: During vehicle driving, the noise signal of passenger ear-sides is affected by many sound sources. Meanwhile, the composition and generation mechanism of these sound sources are complicated. The application of traditional data-driven technology in the noise signal reconstruction process of passenger ear-sides often requires complex signal processing and prior knowledge, thereby limiting its application in signal reconstruction. Thus, a multivariable-based vehicle interior noise time-domain signal reconstruction (MTSR) algorithm based on compressed sensing and deep learning is proposed to address such limitation. Raw data are compressed to acquire samples using the proposed algorithm to reduce the amount of data and realize the adaptive extraction of signal features. A deep neural network model for the noise signal reconstruction of passenger ear-sides is established on the basis of multisource signals of the compressed domain, which is pretrained by a restricted Boltzmann machine for improved reconstruction accuracy. The recovery compression signal method realizes the time-domain signal reconstruction of the passenger ear-sides. The effectiveness of the proposed MTSR algorithm is validated using noise signal sources collected from a vehicle. Compared with the different reconstruction models, the proposed algorithm is superior in reconstruction accuracy and time consumption.