Speech Enhancement using Multiple Transducers

<p>In this thesis, three methods of speech enhancement techniques are investigated with applications in extreme noise environments. Various beamforming techniques are evaluated for their performance characteristics in terms of signal to (distant) noise ratio and tolerance to design imperfections. Two suitable designs are identified with contrasting performance characteristics — the second order differential array, with excellent noise rejection but poor robustness; and a least squares design, with adequate noise rejection and good robustness. Adaptive filters are introduced in the context of a simple noise canceller and later a post-processor for a dual beamformer system. Modifications to the least mean squares (LMS) filter are introduced to tolerate cross-talk between microphones or beamformer outputs. An adaptive filter based post-processor beamforming system is designed and evaluated using a simulation involving speech in noisy environments. The beamforming methods developed are combined with the modified LMS adaptive filter to further reduce noise (if possible) based on correlations between noise signals in a beamformer directed to the talker and a complementary beamformer (nullformer) directed away from the talker. This system shows small, but not insignificant, improvements in noise reduction over purely beamforming based methods. Blind source separation is introduced briefly as a potential future method for enhancing speech in noisy environments. The FastICA algorithm is evaluated on existing data sets and found to perform similarly to the post-processing system developed in this thesis. Future avenues of research in this field are highlighted.</p>

Speech Enhancement using Multiple Transducers

<p>In this thesis, three methods of speech enhancement techniques are investigated with applications in extreme noise environments. Various beamforming techniques are evaluated for their performance characteristics in terms of signal to (distant) noise ratio and tolerance to design imperfections. Two suitable designs are identified with contrasting performance characteristics — the second order differential array, with excellent noise rejection but poor robustness; and a least squares design, with adequate noise rejection and good robustness. Adaptive filters are introduced in the context of a simple noise canceller and later a post-processor for a dual beamformer system. Modifications to the least mean squares (LMS) filter are introduced to tolerate cross-talk between microphones or beamformer outputs. An adaptive filter based post-processor beamforming system is designed and evaluated using a simulation involving speech in noisy environments. The beamforming methods developed are combined with the modified LMS adaptive filter to further reduce noise (if possible) based on correlations between noise signals in a beamformer directed to the talker and a complementary beamformer (nullformer) directed away from the talker. This system shows small, but not insignificant, improvements in noise reduction over purely beamforming based methods. Blind source separation is introduced briefly as a potential future method for enhancing speech in noisy environments. The FastICA algorithm is evaluated on existing data sets and found to perform similarly to the post-processing system developed in this thesis. Future avenues of research in this field are highlighted.</p>

The On-Chip D-LMS Filter Design Method of Wireless Sensor Node Based on FPGA

In the real-time position technology of underground shallow source, the signal denoising performance of wireless sensor nodes directly determines the location speed and accuracy of underground burst point. Because of the complexity and randomness of the underground medium and the fact that underground explosion is a nonstationary transient process, the problems of low convergence rate and poor steady-state performance of the filter exist when the existing LMS algorithm is used for signal denoising. In light of the above concerns, this paper comes up with a signal denoising algorithm and hardware implementation method based on D-LMS (delay-LMS). Firstly, according to the autocorrelation function characteristic of random signal, using the principle that the autocorrelation function time delay characteristic of narrowband signal such as explosion vibration signal is better than that of wideband random signal such as ground noise, the D-LMS filter algorithm is constructed by introducing the time delay parameter. Secondly, the selection method of key parameters in D-LMS hardware implementation is analyzed. Thirdly, the corresponding hardware circuit is designed by FPGA, and the simulation is carried out. Numerical simulation and experimental verification show that compared with the existing LMS improved algorithm, the D-LMS algorithm proposed in this paper has higher denoising stability and better denoising effect. Compared with the signal postprocessing method based on the host computer, the signal denoising speed of this method is significantly improved. This method will provide a powerful theoretical method to solve the problem of high-precision and fast source positioning and provide technical support for the development of high-speed and real-time source positioning instruments.

Real Time Implementation of SIGN LMS Adaptive Filters using Xilinx System Generator

Sign Least Mean Square (SLMS) adaptive filter can adapt dynamically based on corresponding filter output. One of the major applications of adaptive filter is Noise cancellation. In real time applications like medical computing, speed of the process developing hardware is essential hence the hardware realization of SLMS adaptive filter using Xilinx System generator is proposed in this work. The propose architecture aims to reduce convergence rate, path delay and increasing speed. In this work (i) Modified architecture is designed for a 8-tap SLMS adaptive filter and (ii) multiplier less structure for Modified DLMS Filter. The designed architecture tested for ECG signal. The functionality of the algorithm is verified in MATLAB with various ECG data from the MIT-BIH database as input. Both LMS and SLMS are designed, simulated, synthesized and implemented in Virtex-5 FPGA using Xilnix ISE 14.3 . The result shows 5% decrease in total real time router completion and also decrease in the number of adders and subtractors, the maximum combinational path delay has been reduced by 48.84% in Systolic Sign LMS Filter when compared to LMS Filter.

Design of New Reconfigurable Architecture for Implementing a Least Mean Square Finite Impulse Response Filter Using Borrow Select Subtraction (BSLS)

A design of reconfigurable architecture of FIR filter has been implemented using a Least Mean Square (LMS) adaptive filter. LMS adaptive filter is mainly sued for reducing the coefficients of the filter. Generally, a LMS filter contains normal adder, subtractor, mixer and a delay part. Most of the concepts deal with an adder namely Full Adder (FA), Ripple Carry Adder (RCA), Carry Select Adder (CSLA), etc., Instead of using CSLA; Borrow Select Subtractor (BSLS) is used in LMS filter architecture. By using BSLA LMS adaptive filter in a reconfigurable FIR filter architecture in the proposed scheme, the area, power and delay will be reduced. The proposed scheme achieves better performance when compared to an existing scheme. The proposed method is implemented in ModelSim tool and efficiency has been calculated by using the device Virtex 6 Low Power in Xilinx ISE Design Suite 12.4.