LEADER 04641nam 22007695 450 001 9910299851503321 005 20200706074304.0 010 $a3-319-08954-4 024 7 $a10.1007/978-3-319-08954-6 035 $a(CKB)3710000000205459 035 $a(EBL)1783141 035 $a(SSID)ssj0001295608 035 $a(PQKBManifestationID)11843707 035 $a(PQKBTitleCode)TC0001295608 035 $a(PQKBWorkID)11342654 035 $a(PQKB)10791953 035 $a(DE-He213)978-3-319-08954-6 035 $a(MiAaPQ)EBC1783141 035 $a(PPN)179924036 035 $a(EXLCZ)993710000000205459 100 $a20140725d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAdaptive Identification of Acoustic Multichannel Systems Using Sparse Representations /$fby Karim Helwani 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (120 p.) 225 1 $aT-Labs Series in Telecommunication Services,$x2192-2810 300 $aDescription based upon print version of record. 311 $a1-322-13769-2 311 $a3-319-08953-6 320 $aIncludes bibliographical references. 327 $aIntroduction -- Fundamentals of Adaptive Filter Theory -- Spatio-Temporal Regularized Recursive Least Squares Algorithm -- Sparse Representation of Multichannel Acoustic Systems -- Unique System Identification from Projections -- Geometrical Constraints -- Acoustic Echo Suppression -- Conclusion. 330 $aThis book treats the topic of extending the adaptive filtering theory in the context of massive multichannel systems by taking into account a priori knowledge of the underlying system or signal. The starting point is exploiting the sparseness in acoustic multichannel system in order to solve the non-uniqueness problem with an efficient algorithm for adaptive filtering that does not require any modification of the loudspeaker signals. The book discusses in detail the derivation of general sparse representations of acoustic MIMO systems in signal or system dependent transform domains. Efficient adaptive filtering algorithms in the transform domains are presented and the relation between the signal- and the system-based sparse representations is emphasized. Furthermore, the book presents a novel approach to spatially preprocess the loudspeaker signals in a full-duplex communication system. The idea of the preprocessing is to prevent the echoes from being captured by the microphone array in order to support the AEC system. The preprocessing stage is given as an exemplarily application of a novel unified framework for the synthesis of sound figures. Finally, a multichannel system for the acoustic echo suppression is presented that can be used as a postprocessing stage for removing residual echoes. As first of its kind, it extracts the near-end signal from the microphone signal with a distortionless constraint and without requiring a double-talk detector. 410 0$aT-Labs Series in Telecommunication Services,$x2192-2810 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aComputer input-output equipment 606 $aElectrical engineering 606 $aAcoustics 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aInput/Output and Data Communications$3https://scigraph.springernature.com/ontologies/product-market-codes/I12042 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 606 $aAcoustics$3https://scigraph.springernature.com/ontologies/product-market-codes/P21069 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aComputer input-output equipment. 615 0$aElectrical engineering. 615 0$aAcoustics. 615 14$aSignal, Image and Speech Processing. 615 24$aInput/Output and Data Communications. 615 24$aCommunications Engineering, Networks. 615 24$aAcoustics. 676 $a621.3822 700 $aHelwani$b Karim$4aut$4http://id.loc.gov/vocabulary/relators/aut$0721041 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299851503321 996 $aAdaptive Identification of Acoustic Multichannel Systems Using Sparse Representations$91413116 997 $aUNINA