Open AccessMulti‐scale audio super resolution via deep pyramid wavelet convolutional neural network
Author(s) -
Si Binqiang,
Luo Dongqi,
Zhu Jihong
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12180
Subject(s) - wavelet , computer science , convolutional neural network , pyramid (geometry) , artificial intelligence , wavelet transform , focus (optics) , signal (programming language) , speech recognition , pattern recognition (psychology) , activation function , artificial neural network , mathematics , physics , geometry , optics , programming language
In this letter, a pyramid wavelet convolutional neural network for audio super resolution is presented. Since the audio signal is non‐stationary, previous convolutional neural network based approaches may fail in capturing the details, these method usually focus on the global approximation error and thus produce over smooth results. To cope with this issue, it is suggested to predict the wavelet coefficients of the audio signal, and reconstruct the signal from these coefficients stage by stage rather. The prediction errors of the wavelet coefficients are included to the loss function to force the model to capture the detail components. Experimental results show that the approach, training on the VCTK public dataset, achieves more appealing results than state‐of‐the‐art methods.