Adaptive Noise Cancellation Algorithm Research Articles

Adaptive Algorithms for Active Noise Cancellation Using Time-Varying Convergence Coefficient

— The performance of an ANC system depends on the convergence factor. A suitable value of convergence coefficient is extremely important as it impacts both the speed of convergence and the stability of the adaptive algorithm. Traditionally, in active noise control systems fixed convergence factor is used. In this paper, a novel approach of time-varying convergence factor μ is used and the results are compared with traditional feedforward and feedback noise control systems. This algorithm leads to faster convergence and provides reduced mean-squared error compared to the conventional fixed parameter algorithm.

Study of Different Adaptive Filter Algorithms for Noise Cancellation in Real-Time Environment

Adaptive filters are used in the situation where the filter coefficients have to be changed simultaneously according to the requirement. Adaptive filters are needed for fast convergence rate and low mean square error. Many algorithms have been proposed and proved that they have better convergence speed and tracking abilities. This paper shows the ability of adaptive filter for noise cancellation i.e., estimating the desired speech corrupted by unwanted signal i.e., noise. This paper is going to compare the performance of adaptive algorithms for noise cancellation in real time signals like recorded speech with different background noise. In the existing papers the authors have taken the input signal as sinusoidal signal etc. In order to measure the performance step size is the main factor for the convergence speed and mean square error. Many analyses proved that RLS algorithm had faster convergence speed and smaller steady state error compared with basic LMS algorithm and normalized LMS algorithm (NLMS). The existing simulation results enable to measure the performance of filter and show the convergence speed improvement when using RLS algorithm, NLMS algorithm and LMS algorithm.

Hardware Implementation of Adaptive Algorithms forNoise Cancellation

Hardware Implementation of Adaptive Algorithms forNoise Cancellation

Adaptive Noise Cancellation for speech Employing Fuzzy and Neural Network

Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications such as noise cancellation. Noise cancellation is a common occurrence in today telecommunication systems. The LMS algorithm which is one of the most efficient criteria for determining the values of the adaptive noise cancellation coefficients are very important in communication systems, but the LMS adaptive noise cancellation suffers response degrades and slow convergence rate under low Signal-to-Noise ratio (SNR) condition. This paper presents an adaptive noise canceller algorithm based fuzzy and neural network. The major advantage of the proposed system is its ease of implementation and fast convergence. The proposed algorithm is applied to noise canceling problem of long distance communication channel. The simulation results showed that the proposed model is effectiveness.

Adaptive Noise Cancellation for speech Employing Fuzzy and Neural Network

Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications such as noise cancellation. Noise cancellation is a common occurrence in today telecommunication systems. The LMS algorithm which is one of the most efficient criteria for determining the values of the adaptive noise cancellation coefficients are very important in communication systems, but the LMS adaptive noise cancellation suffers response degrades and slow convergence rate under low Signal-to-Noise ratio (SNR) condition. This paper presents an adaptive noise canceller algorithm based fuzzy and neural network. The major advantage of the proposed system is its ease of implementation and fast convergence. The proposed algorithm is applied to noise canceling problem of long distance communication channel. The simulation results showed that the proposed model is effectiveness.

Stability Analysis for the Generalized Sidelobe Canceller

One approach for reducing noise from non-target locations in array beamformer applications is to use an adaptive noise cancellation algorithm referred to as the Generalized Sidelobe Canceller (GSC). An analysis is performed to derive a closed-form expression showing the relationship between stability, the number of array channels, adaptive step size ?, and forgetting factor s. The result is verified by applying the GSC to experimental data with a range of values for ?, s, and array channels to show when the GSC becomes unstable.

Low-cost, high-fidelity, adaptive cancellation of periodic 60 Hz noise

A common method to eliminate unwanted power line interference in neurobiology laboratories where sensitive electronic signals are measured is with a notch filter. However a fixed-frequency notch filter cannot remove all power line noise contamination since inherent frequency and phase variations exist in the contaminating signal. One way to overcome the limitations of a fixed-frequency notch filter is with adaptive noise cancellation. Adaptive noise cancellation is an active approach that uses feedback to create a signal that when summed with the contaminated signal destructively interferes with the noise component leaving only the desired signal. We have implemented an optimized least mean square adaptive noise cancellation algorithm on a low-cost 16MHz, 8-bit microcontroller to adaptively cancel periodic 60 Hz noise. In our implementation, we achieve between 20 and 25dB of cancellation of the fundamental 60Hz noise component.

Speech Enhancement by Multichannel Crosstalk Resistant ANC and Improved Spectrum Subtraction

A scheme combining multichannel crosstalk resistant adaptive noise cancellation (MCRANC) algorithm and improved spectrum subtraction (ISS) algorithm is presented to enhance noise carrying speech signals. The scheme would permit locating the microphones in close proximity by virtue of using MCRANC which has the capability of removing the crosstalk effect. MCRANC would also permit canceling out nonstationary noise and making the residual noise more stationary for further treatment by ISS algorithm. Experimental results have indicated that this scheme outperforms many commonly used techniques in the sense of SNR improvement and music effect reduction which is an inevitable byproduct of the spectrum subtraction algorithm.

Adaptive noise cancellation using enhanced dynamic fuzzy neural networks

In this paper, a novel adaptive noise cancellation algorithm using enhanced dynamic fuzzy neural networks (EDFNNs) is described. In the proposed algorithm, termed EDFNN learning algorithm, the number of radial basis function (RBF) neurons (fuzzy rules) and input-output space clustering is adaptively determined. Furthermore, the structure of the system and the parameters of the corresponding RBF units are trained online automatically and relatively rapid adaptation is attained. By virtue of the self-organizing mapping (SOM) and the recursive least square error (RLSE) estimator techniques, the proposed algorithm is suitable for real-time applications. Results of simulation studies using different noise sources and noise passage dynamics show that superior performance can be achieved.

Narrowband Interference in Pilot Symbol Assisted OFDM Systems

The effect of narrowband interference on pilot symbol assisted detection and synchronization is discussed. It is shown both analytically and by computer simulation that pilot symbol detectors are particularly susceptible to narrowband interference. It is shown also that pilot symbol assisted frequency offset estimation is detrimentally affected by narrowband interference. The normalized least mean squares (N-LMS) adaptive noise cancellation algorithm, with only a small number of filter taps, is shown to perform well in suppressing narrowband interference in pilot symbol detectors. The combination of two-stage detection and the N-LMS algorithm is shown to be effective in producing interference-tolerant pilot symbol detection in OFDM systems.

Adaptive noise cancellation in a multimicrophone system for distortion product otoacoustic emission acquisition

This study focuses on adaptive noise cancellation (ANC) techniques for the acquisition of distortion product otoacoustic emissions (DPOAEs). Otoacoustic emissions (OAEs) are very low level sounds produced by the outer hair cells of normal cochleas, spontaneously or in response to sound stimulation as a byproduct of a frequency and threshold sensitivity increasing mechanism. Current OAE recording systems rely on test probe noise attenuation and synchronous ensemble averaging for increasing signal-to-noise ratios (SNRs). The efficiency of an ANC algorithm for noise suppression was investigated using three microphones: one placed in the test ear, one in the nontest ear for internal noise reference; one near the subject's head for external noise reference. The system proposed was tested with simulations, off-line averaging and real-time implementation of the ANC algorithm. Simulation results showed that the technique had a potential noise reduction capability of 24 dB for complex multifrequency noise signals. Off-line results were positive, with a mean SNR improvement of 4.9 dB. Real-time results indicated that the use of an ANC algorithm in combination with standard averaging methods can reduce noise levels by as much as 10 dB beyond that obtained with standard noise reduction methods and probe attenuation alone.

Qualitative and quantitative results from a speech enhancement scheme assessed by hearing-impaired subjects

A multi-microphone sub-band adaptive system for binaural preprocessing of speech signals for potential processing in future hearing aids has been investigated. Results are presented from both quantitative and qualitative assessments of the performance of the processing scheme as recorded from hearing-impaired subjects. The quantitative assessment is based on intelligibility testing of the algorithm using real-room recordings, T60=0.3 s, with multi-talker babble as the unwanted noise source. This evaluation has been conducted by using the four-alternative auditory feature test developed by Foster and Haggard [J. Br. Audiol. 21, 165–174 (1987)]. A corresponding mean opinion score on speech quality from each test subject provides the qualitative analysis. A response time measure was included with the qualitative measure. The processing scheme uses the least mean squares (LMS) [S. D. Sterns and R. A. David, Signal Processing Algorithms (Prentice–Hall, Englewood Cliffs, NJ, 1988)] adaptive noise cancellation algorithm in frequency-limited subbands. The inputs from each microphone are split into 16 contiguous subbands using a cochlear distribution according the function provided by Greenwood [J. Acoust. Soc. Am. 87, 2592–2605 (1990)]. Each frequency-limited subband is processed using a LMS adaptive noise cancellation filter operating in an intermittent or continuous mode depending on input signal characteristics. The results show a statistically significant and practically valuable improvement in both intelligibility and perceived speech quality using the proposed scheme.

Comparison of wideband LMS, subband LMS, and a nonlinear entropic neural approach to adaptive noise cancellation for speech enhancement

An empirical comparison of three adaptive algorithms for speech enhancement in noisy reverberant conditions is presented. The subband least mean square (LMS) [E. Toner and D. R. Campbell, Speech Commun. 12, 253–259 (1993)] and minimum entropy noise reduction schemes [M. Girolami, Electron. Lett. 33(17), 1437–1438 (1997)] are compared with a reference wideband LMS approach [S. D. Stearns and R. A. David, Signal Processing Algorithms (Prentice–Hall, Englewood Cliffs, NJ, 1988)]. All three methods were applied to the enhancement of speech corrupted with speech-shaped noise. The schemes were tested using simulated anechoic and real-room reverberant (T60=0.3 s) environments. The anechoic and real-room recordings were binaural including head shadow and diffraction effects. The subband LMS adaptive processing scheme uses the LMS adaptive noise cancellation algorithm in frequency-limited subbands. The inputs from each microphone are split into 16 contiguous subbands using a cochlear distribution according the function provided by Greenwood [J. Acoust. Soc. Am. 87, 2592–2605 (1990)]. Each frequency-limited subband is processed using a LMS adaptive noise cancellation filter operating in an intermittent or continuous mode depending on input signal characteristics. The neural network approach is motivated by temporally sensitive hebbian super-synapses, which may form sparse representations based on commonly occurring spatio-temporal input patterns. The results for real-room signals demonstrate statistically equivalent performance of the subband LMS and wideband neural method, both of which prove superior to the wideband LMS algorithm.

Effect of microphone placement on adaptive noise cancellation methods with machine ball bearing measurements to detect bearing flaws in reverberant environments

Sound measurements on operating machinery in reverberant environments are difficult to analyze. Adaptive noise algorithms [C. C. Tan, IECON, 703–708 (1985)] are available to reduce the amount of background noise in the measured data. Due to the reverberation of the environment, proper placement of the microphones is important to achieve the greatest noise cancellation from the algorithm. Measurements were taken on bearings with known flaws under different loads and speeds. Results of these measurements will show the effectiveness of sound pressure measurements, and sound intensity measurements, to detect bearing flaws with the help of the adaptive noise cancellation algorithm when the microphones are properly placed.

The detection of transient‐like signals in the presence of a spatially inhomogeneous, nonstationary noise field

AbstractWe will consider the problem of detecting transient‐like signals by means of a spatially distributed array of sensors embedded in an inhomogeneous, nonstationary noise field. If each element of the array has an independent, uncorrupted reference sensor to estimate the noise statistics, then conventional adaptive noise cancellation algorithms can be used to improve the detection process. However, because of practical real‐world constraints, independent, uncorrupted reference sensors for the noise might not be available. Thus, the applicability of conventional adaptive noise cancellation techniques is in question. This paper will discuss the development of a knowledge‐based signal‐processing system that uses Artificial Intelligence (Al) methodologies to adaptively cancel inhomogeneous, nonstationary noise from a distributed array of passive sensors that is constrained to have no noise‐reference sensors.