2022/03/11 by Jun Qi, Qi, Jun, Chao-Han Huck Yang +5 · 2 citations
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Electrical engineering #Human Pose and Action Recognition #Machine Learning (cs.LG) #Sound (cs.SD) #Tensor decomposition and applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2203.06031
openalex publication_date 2022/03/11 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance. Firstly, we exploit a low-rank tensor-train deep neural network (TT-DNN) to build an end-to-end deep learning pipeline, namely LR-TT-DNN. Secondly, a hybrid model combining LR-TT-DNN with a convolutional neural network (CNN), which is denoted as CNN+(LR-TT-DNN), is set up to boost the performance. Instead of randomly assigning large TT-ranks for TT-DNN, we leverage Riemannian gradient descent to determine a TT-DNN associated with small TT-ranks. Furthermore, CNN+(LR-TT-DNN) consists of convolutional layers at the bottom for feature extraction and several TT layers at the top to solve regression and classification problems. We separately assess the LR-TT-DNN and CNN+(LR-TT-DNN) models on speech enhancement and spoken command recognition tasks. Our empirical evidence demonstrates that the LR-TT-DNN and CNN+(LR-TT-DNN) models with fewer model parameters can outperform the TT-DNN and CNN+(TT-DNN) counterparts.