Real-Time Implementation of New Adaptive Beamformer Sensor Array For Speech Enhancement in Hearing Aid

The objective of this paper is to provide improved real-time noise canceling performance while keeping the high quality of enhanced speech by using new robust adaptive beamformer. The proposed beamformer uses a new adaptive blocking matrix, which consists of linear prediction error filters (LPEFs). It also employ a multi-channel noise canceller with adaptive noise estimation filters (ANEFs) used for inverse modeling. The entire proposed system has been implemented in real-time in a real environment using National Instruments NI- PXI-1042Q controller system and data acquisition card NI-PXI-4472. Practical results show that the proposed beamformer produces results that are significantly favorable than standard GSC beamformer.

[1]  Yutaka Fukui,et al.  A new noise reduction method using linear prediction error filter and adaptive digital filter , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).

[2]  Masato Miyoshi,et al.  Inverse filtering of room acoustics , 1988, IEEE Trans. Acoust. Speech Signal Process..

[3]  O. Hoshuyama,et al.  A robust adaptive beamformer for microphone arrays with a blocking matrix using constrained adaptive filters , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[4]  S. Nordholm,et al.  Adaptive beamforming: Spatial filter designed blocking matrix , 1994 .

[5]  L. J. Griffiths,et al.  An alternative approach to linearly constrained adaptive beamforming , 1982 .

[6]  Karl-Dirk Kammeyer,et al.  Theoretical noise reduction limits of the generalized sidelobe canceller (GSC) for speech enhancement , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[7]  Ehud Weinstein,et al.  Signal enhancement using beamforming and nonstationarity with applications to speech , 2001, IEEE Trans. Signal Process..

[8]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .