Source Separation and Machine Learning
ຕ.ລ. 2018 · Academic Press
ປຶ້ມອີບຸກ
384
ໜ້າ
family_home
ມີສິດ
info
reportບໍ່ໄດ້ຢັ້ງຢືນການຈັດອັນດັບ ແລະ ຄຳຕິຊົມ ສຶກສາເພີ່ມເຕີມ
ກ່ຽວກັບປຶ້ມ e-book ນີ້
Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation. - Emphasizes the modern model-based Blind Source Separation (BSS) which closely connects the latest research topics of BSS and Machine Learning - Includes coverage of Bayesian learning, sparse learning, online learning, discriminative learning and deep learning - Presents a number of case studies of model-based BSS (categorizing them into four modern models - ICA, NMF, NTF and DNN), using a variety of learning algorithms that provide solutions for the construction of BSS systems
ກ່ຽວກັບຜູ້ຂຽນ
Jen-Tzung Chien received his Ph.D. in electrical engineering from National Tsing Hua University, Taiwan in 1997. He is now with the Department of Electrical and Computer Engineering and the Department of Computer Science at the National Chiao Tung University, Taiwan, where he is currently the Chair Professor. He was the visiting professor at the IBM T. J. Watson Research Center, Yorktown Heights, NY in 2010. Dr. Chien has served as the associate editor of the IEEE Signal Processing Letters in 2008-2011, the tutorial speaker of the ICASSP in 2012, 2015, 2017, the INTERSPEECH in 2013, 2016, the COLING in 2018, and the general chair of the IEEE International Workshop on Machine Learning for Signal Processing in 2017. He received the Best Paper Award of the IEEE Automatic Speech Recognition and Understanding Workshop in 2011 and the AAPM Farrington Daniels Paper Award in 2018. He is currently serving as an elected member of the IEEE Machine Learning for Signal Processing Technical Committee.
ໃຫ້ຄະແນນ e-book ນີ້
ບອກພວກເຮົາວ່າທ່ານຄິດແນວໃດ.
ອ່ານຂໍ້ມູນຂ່າວສານ
ສະມາດໂຟນ ແລະ ແທັບເລັດ
ຕິດຕັ້ງ ແອັບ Google Play Books ສຳລັບ Android ແລະ iPad/iPhone. ມັນຊິ້ງຂໍ້ມູນໂດຍອັດຕະໂນມັດກັບບັນຊີຂອງທ່ານ ແລະ ອະນຸຍາດໃຫ້ທ່ານອ່ານທາງອອນລາຍ ຫຼື ແບບອອບລາຍໄດ້ ບໍ່ວ່າທ່ານຈະຢູ່ໃສ.
ແລັບທັອບ ແລະ ຄອມພິວເຕີ
ທ່ານສາມາດຟັງປຶ້ມສຽງທີ່ຊື້ໃນ Google Play ໂດຍໃຊ້ໂປຣແກຣມທ່ອງເວັບຂອງຄອມພິວເຕີຂອງທ່ານໄດ້.
eReaders ແລະອຸປະກອນອື່ນໆ
ເພື່ອອ່ານໃນອຸປະກອນ e-ink ເຊັ່ນ: Kobo eReader, ທ່ານຈຳເປັນຕ້ອງດາວໂຫຼດໄຟລ໌ ແລະ ໂອນຍ້າຍມັນໄປໃສ່ອຸປະກອນຂອງທ່ານກ່ອນ. ປະຕິບັດຕາມຄຳແນະນຳລະອຽດຂອງ ສູນຊ່ວຍເຫຼືອ ເພື່ອໂອນຍ້າຍໄຟລ໌ໄໃສ່ eReader ທີ່ຮອງຮັບ.
