Design and Implementation of Dual-Microphone Adaptive-Active Noise Cancellation System for De-Noising Speech Signal

• Autorzy: Sharifi-Tehrani, O. , Ghafarioun, E.

Warianty tytułu

PL

Projekt i wykorzystanie podwójnego mikrofonu w adaptacyjnej metodzie usuwania szumu z sygnału mowy

• Języki publikacji: EN

Abstrakty

EN

Active Noise Control (ANC) has become an important field of research in recent decades. Noise Control in industrial environments and conference halls as well as in communication systems has been studied under the title adaptive-active noise cancellation-control (AANCC) and the results of these studies have been used in practical applications. Reducing time dependent noise is one of the ways recommended for speech enhancement. Here we have introduced an artificial neural network called ADALINE as a smart dual microphone active noise control system. This artificial neural network identifies sources of noise and interference during its training phase and adjusts accordingly. In this way the system reduces the input signal noise. Tests and implementations presented here are based on speech in Persian language and cumulative white Gaussian noise and the interference is assumed to be of the cosine type.

PL

Przedstawiono analizę szumów w otoczeniu przemysłowym i w salach konferencyjnych a następnie przedstawiono metody adaptacyjnych metod redukcji szumów. Szczególną uwagę zwrócono na szum zależny od czasu. Zastosowano metodę podwójnego mikrofonu i wykorzystano sieci neuronowe. Sieć identyfikuje źródło szumu i zakłóceń. Metodę sprawdzono doświadczalnie.

Słowa kluczowe

PL

redukcja szumów; sieć neuronowa ADALINE; filtry adaptacyjne

EN

signal enhancement; noise reduction; ADALINE network; adaptive-active filter; LMS algorithm

• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 2
• Strony: 258-261
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Sharifi-Tehrani, O.

autor

Ghafarioun, E.

• 1,2- Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran,

Bibliografia

• [1] O. Sharifi-Tehrani and M. Ashourian, An FPGA-Based Implementation of ADALINE Neural Network with Low Resource Utilization and Fast Convergence, Przeglad Elektrotechniczny (Electrical Review), 86 (2010), No. 12, 288- 292.
• [2] O. Sharifi-Tehrani, M. Ashourian and P. Moallem, An FPGABased Implementation of Fixed-Point Standard-LMS Algorithm with Low Resource Utilization and Fast Convergence, Inter. Rev. on Comp. and Soft. (IReCOS), 5 (2010), No. 4, 436-444.
• [3] V.R. Mustafa, et al., Design and implementation of least mean square adaptive filter on Altera Cyclone II field programmable gate array for active noise control, IEEE Sym. on Industrial Electronics Applications - ISIEA, Kuala Lumpur, Malaysia, (2009), 479-484.
• [4] M.T. Akhtar, Improving Performance of Active Noise Control Systems in the Presence of Uncorrelated Periodic Disturbance at Error Microphone, IEEE Transaction on Audio, Speech and Language Processing, 35 (2009), No. 3, 2041-2044.
• [5] M. Bahoura and H.Ezzaidi, FPGA-implementation of a sequential adaptive noise canceller using Xilinx system generator, Inter. Conf. on Microelectronics - ICM, Marrakech, (2009), 213-216.
• [6] M.T. Akhtar, On Active Noise Control Systems with Online Acoustic Feedback Path Modeling, IEEE Transaction on Audio, Speech and Language Processing, 15 (2007), No. 2, 593-600.
• [7] E. Ghafarioun, O. Sharifi-Tehrani and T. Khaluei, An Evaluation of Probability of Transmitted Pilot Bit Error on Multi- Path Fading Channels and Using it in Estimation the Mobile Station Speed, Inter. Journ. on Comm. Anten. and Prop. (IReCAP), 1 (2011), No. 1.
• [8] B.S. Kirei, M.D. Topa, I. Muresan, I. Homana and N. Toma, Blind Source Separation for Convolutive Mixtures with Neural Networks, Advances in Electrical and Computer Engineering (AECE), 11 (2011), No. 1, 63-68.
• [9] N.A. Ahmad and F.Z. Okwonu, Least Squares Problem for Adaptive Filtering, Australian Journal of Basic and Applied Sciences, 5 (2011), No. 3, 69-74.
• [10] A. Sergi and A. Cichocki, Hyper Radial Basis Function Neural Networks for Interference Cancellation with Nonlinear Processing of Reference Signal, Journal of Digital Signal Processing, 11 (2001), 204-221.
• [11] P.S.R. Diniz, Adaptive Filtering: Algorithms and Practical Implementation, Kluwer, Boston, 2nd edition, 2002.
• [12] I. Mcloughlin, Applied Speech and Audio Processing, Cambridge University Press, Singapore, (2009).