9783838338477 - BLIND SOURCE SEPARATION USING FREQUENCY INDEPENDENT COMPONENT ANALYSIS di Okwelume, Gozie; K. Ezeude, Anayo (9 risultati)

Condizione: New. pp. 64.

Taschenbuch. Condizione: Neu. BLIND SOURCE SEPARATION USING FREQUENCY INDEPENDENT COMPONENT ANALYSIS | Gozie Okwelume (u. a.) | Taschenbuch | 64 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838338477 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The need for speech enhancement is very important, because of the acoustic environment we are living in, which is composed of noise and other atmospheric disturbances, and this makes it almost impossible to record a speech signal in pure form. In most of the mixed signals there is usually no information about each source. In such situation the estimates of the original source signals is done based on the information of the received mixed signals, therefore the approach to be adopted in such cases to separate the signals must be one that does it blindly, thus the method Blind Source Separation is used in this work. Our thesis work focuses on Frequency domain Blind Source Separation (BSS) in which the received mixed signals are converted into the frequency domain and Independent Component Analysis (ICA) is applied at each frequency bin. Our main target in this project is to solve the permutation and scaling ambiguities in real time applications using the method proposed by Minje et al in [12]. Our results show that this method works better in an 'offline' mixtures than in real time and lastly we gave some suggestions to improve the results. 64 pp. Englisch.

Condizione: New. Print on Demand pp. 64 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condizione: New. PRINT ON DEMAND pp. 64.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Okwelume GozieGozie Okwelume, received his MSc degree in Elec. Eng. from Blekinge Inst. of Tech. Sweden in 2007. He worked as a Systems Manager at Hornitex Nigeria LTD. Kingsley Ezeude, received his MSc degree in Elec. Eng. from Blek.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The need for speech enhancement is very important, because of the acoustic environment we are living in, which is composed of noise and other atmospheric disturbances, and this makes it almost impossible to record a speech signal in pure form. In most of the mixed signals there is usually no information about each source. In such situation the estimates of the original source signals is done based on the information of the received mixed signals, therefore the approach to be adopted in such cases to separate the signals must be one that does it blindly, thus the method Blind Source Separation is used in this work. Our thesis work focuses on Frequency domain Blind Source Separation (BSS) in which the received mixed signals are converted into the frequency domain and Independent Component Analysis (ICA) is applied at each frequency bin. Our main target in this project is to solve the permutation and scaling ambiguities in real time applications using the method proposed by Minje et al in [12]. Our results show that this method works better in an 'offline' mixtures than in real time and lastly we gave some suggestions to improve the results.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The need for speech enhancement is very important, because of the acoustic environment we are living in, which is composed of noise and other atmospheric disturbances, and this makes it almost impossible to record a speech signal in pure form. In most of the mixed signals there is usually no information about each source. In such situation the estimates of the original source signals is done based on the information of the received mixed signals, therefore the approach to be adopted in such cases to separate the signals must be one that does it blindly, thus the method Blind Source Separation is used in this work. Our thesis work focuses on Frequency domain Blind Source Separation (BSS) in which the received mixed signals are converted into the frequency domain and Independent Component Analysis (ICA) is applied at each frequency bin. Our main target in this project is to solve the permutation and scaling ambiguities in real time applications using the method proposed by Minje et al in [12]. Our results show that this method works better in an 'offline' mixtures than in real time and lastly we gave some suggestions to improve the results.