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Titlebook: Audio Source Separation; Shoji Makino Book 2018 Springer International Publishing AG 2018 audio source separation methods.non-negative mat

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发表于 2025-3-21 17:33:33 | 显示全部楼层 |阅读模式
期刊全称Audio Source Separation
影响因子2023Shoji Makino
视频video
发行地址Offers the first comprehensive treatment of audio source separation based on non-negative matrix factorization, deep neural network, and sparse component analysis.Describes fundamentals and applicatio
学科分类Signals and Communication Technology
图书封面Titlebook: Audio Source Separation;  Shoji Makino Book 2018 Springer International Publishing AG 2018 audio source separation methods.non-negative mat
影响因子.This book provides the first comprehensive overview of the fascinating topic of audio source separation based on non-negative matrix factorization, deep neural networks, and sparse component analysis. .The first section of the book covers single channel source separation based on non-negative matrix factorization (NMF). After an introduction to the technique, two further chapters describe separation of known sources using non-negative spectrogram factorization, and temporal NMF models. In section two, NMF methods are extended to multi-channel source separation. Section three introduces deep neural network (DNN) techniques, with chapters on multichannel and single channel separation, and a further chapter on DNN based mask estimation for monaural speech separation. In section four, sparse component analysis (SCA) is discussed, with chapters on source separation using audio directional statistics modelling, multi-microphone MMSE-based techniques and diffusion map methods.. .The book brings together leading researchers to provide tutorial-like and in-depth treatments on major audio source separation topics, with the objective of becoming the definitive source for a comprehensive, aut
Pindex Book 2018
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Carl C. Gaither,Alma E. Cavazos-Gaithertation of the GSC’s blocking matrix und interference and noise canceler coefficients. Finally, we establish relations between the proposed method and other well-known multichannel linear filter approaches for signal extraction based on second-order-statistics, and demonstrate the effectiveness of th
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Determined Blind Source Separation with Independent Low-Rank Matrix Analysis,, namely, IVA and ILRMA are identical to a special case of MNMF, which employs a rank-1 spatial model. Experimental results show the efficacy of ILRMA compared with IVA and MNMF in terms of separation accuracy and convergence speed.
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Informed Spatial Filtering Based on Constrained Independent Component Analysis,tation of the GSC’s blocking matrix und interference and noise canceler coefficients. Finally, we establish relations between the proposed method and other well-known multichannel linear filter approaches for signal extraction based on second-order-statistics, and demonstrate the effectiveness of th
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Audio Source Separation978-3-319-73031-8Series ISSN 1860-4862 Series E-ISSN 1860-4870
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