K.G. Gunale

Bio: K.G. Gunale is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Adaptive filter & Time domain. The author has an hindex of 1, co-authored 1 publications receiving 4 citations.

Frequency Domain Adaptive Filter using FFT Algorithm for Acoustic Echo Cancellation

Abstract: Adaptive Filters are generally implemented in the time domain which works well in most scenarios however in many applications the impulse response becomes too long, increasing the complexity of the filter beyond a level where it can no longer be implemented efficiently in the time domain. An example of where this can happen would be in acoustic echo cancellation applications in hands free telephony. However there exists an alternative solution and that is to implement the filters in the frequency domain. The Discrete Fourier Transform or more specifically the Fast Fourier Transform (FFT) allows the conversion of signals from the time domain to the frequency domain in an efficient manner. Acoustic echo cancellation of the frequency domain adaptive filter is compared with time domain adaptive filter. Again results are compared for both frequency domain and time domain adaptive filters with different step sizes.

Echo cancellers and echo cancelling methods

Abstract: Methods and systems for updating the adaptive filter of an echo canceller. A method of updating the adaptive filter of an echo canceller in which an estimated echo is resolved from a received signal and then subtracted from an incoming echo-contaminated signal so as to produce a filtered output signal, includes: obtaining a corrected impulse response of an echo reconstruction filter (ERF); calculating specified decision measures usable to decide whether to prospectively apply the corrected ERF impulse response or a current ERF impulse response; determining whether application of the corrected ERF impulse response would result in improved echo cancellation; and updating the ERF to apply the corrected impulse response, when it is determined that the updating would result in a lower residual echo.

Frequency-Domain RF Self-Interference Cancellation for In-Band Full-Duplex Communications

Abstract: Wireless backhaul has recently gained a significant amount of interest as a cost-effective solution in comparison with conventional backhaul technologies with dedicated microwave links or fiber optics. Self-interference cancellation (SIC) is an enabling technology that allows wireless backhaul to operate in the more spectrum-efficient in-band full-duplex (IBFD) operation mode instead of the out-of-band mode. Compared to Wi-Fi IBFD transceivers, wireless in-band backhaul systems face some unique challenges, such as significantly higher transmission power and much larger propagation delay spread for the self-interference signal, especially in the low-frequency bands under 1 GHz, which often prevent accurate SIC performance. The SIC is often implemented with an interference-cancelling filter, where the filter weights are essentially the channel estimates of the self-interference signals. In this paper, a frequency-domain Radio Frequency (RF) SIC (RF-SIC) framework with a novel filter weight optimization algorithm is proposed to tackle the challenges of wireless in-band backhaul. The proposed RF-SIC does not require a dedicated training phase which needs to stop the transmission of the backhaul signal. Moreover, it has the capability of tracking the self-interference channel variation since the filter weights are updated in a block-by-block fashion.

Frequency-Domain RF Self-Interference Cancellation for In-Band Full-Duplex Communications

Abstract: Wireless backhaul has recently gained a significant amount of interest as a cost-effective solution in comparison with conventional backhaul technologies with dedicated microwave links or fiber optics. Self-interference cancellation (SIC) is an enabling technology that allows wireless backhaul to operate in the more spectrum-efficient in-band full-duplex (IBFD) operation mode instead of the out-of-band mode. Compared to Wi-Fi IBFD transceivers, wireless in-band backhaul systems face some unique challenges, such as significantly higher transmission power and much larger propagation delay spread for the self-interference signal, especially in the low-frequency bands under 1 GHz, which often prevent accurate SIC performance. The SIC is often implemented with an interference-cancelling filter, where the filter weights are essentially the channel estimates of the self-interference signals. In this paper, a frequency-domain Radio Frequency (RF) SIC (RF-SIC) framework with a novel filter weight optimization algorithm is proposed to tackle the challenges of wireless in-band backhaul. The proposed RF-SIC does not require a dedicated training phase which needs to stop the transmission of the backhaul signal. Moreover, it has the capability of tracking the self-interference channel variation since the filter weights are updated in a block-by-block fashion.

Novel based Method to Adaptive Algorithm for Acoustic Echo Cancellation of Speech signal in an auditorium

Abstract: In today's technological society, human computer interactions are ever increasing. In many new systems, voice recognition platforms are implemented to give users more convenient ways of operating equipment and systems. To improve the audibility of the speech, the noise and acoustic echo must be removed from the speech signal. In this paper, we presented a new adaptive algorithm in the frequency domain for acoustic echo cancellation of speech signal in an auditorium. The RLS algorithm, the forgetting factor remains constant, which is utilized for the stability of the adaptive algorithm. However, the constant value of the forgetting factor will not support for the sensitive system. The value of the forgetting factor depends on the echo and reverberation. In an auditorium speech, the echo and reverberation signals are not in a stable manner since the constant value of forgetting factor is not a perfect solution for the removing the echo and reverberation. In order to solve this problem we presented average recursive least square adaptive algorithm, which produces the flexible forgetting factor in a min-max manner. The estimated echo values are constructed with the aid of combined feature of the min-max manner, which leads to increase the quality of the speech signal. Finally, our proposed algorithm is implemented using MATLAB and the experimental results showed that the proposed ARLS algorithm outperformed than the existing RLS algorithm.

New Technique for Recursive Least Square Adaptive Algorithm for Acoustic Echo Cancellation of Speech signal in an auditorium

Abstract: In today's technological society, human computer interactions are ever increasing. In many new systems, voice recognition platforms are implemented to give users more convenient ways of operating equipment and systems. To improve the audibility of the speech, the noise and acoustic echo must be removed from the speech signal. In this paper, we presented a new adaptive algorithm in the frequency domain for acoustic echo cancellation of speech signal in an auditorium. The RLS algorithm, the forgetting factor remains constant, which is utilized for the stability of the adaptive algorithm. However, the constant value of the forgetting factor will not support for the sensitive system. The value of the forgetting factor depends on the echo and reverberation. In an auditorium speech, the echo and reverberation signals are not in a stable manner since the constant value of forgetting factor is not a perfect solution for the removing the echo and reverberation. In order to solve this problem we presented average recursive least square adaptive algorithm, which produces the flexible forgetting factor in a min-max manner. The estimated echo values are constructed with the aid of combined feature of the min- max manner, which leads to increase the quality of the speech signal. Finally, our proposed algorithm is implemented using MATLAB and the experimental results showed that the proposed ARLS algorithm outperformed than the existing RLS algorithm.