Adaptive noise cancelling: Principles and applications (original) (raw)
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Development of two-input adaptive noise canceller for wideband and narrowband noise signals
International Journal of Speech Technology, 2017
environments. Intense background noise, however, often corrupts speech and degrades the performance of many communication systems. Thus a large variety of noise reduction techniques have been proposed for reducing background noise in noisy environment, see (Narendra and Han 2012; Moir and Harris 2012; Niedźwiecki and Meller 2012; Roshahliza 2017; Jayakumar et al. 2016) and references therein. The recent noise reduction techniques prefer multi-microphone systems (Bitzer et al. 2001; Mohammed 2009; Touazi and Debyeche 2017), over singlemicrophone systems (Boll 1979; Sasaoka et al. 2005) due to the obvious advantages provided by the former over the later. A multi-microphone system can be considered as a directional microphone with a zero response (null) in the noise direction. However, the microphone array involves increased cost in the form of more microphones, D/A converters, memory, signal processing power, etc. On the other hand, spectral subtraction method (Boll 1979) is known to use single-microphone system. However, in the spectral subtraction method, the musical tones arise from residual noise and it requires the speech/non-speech section detector under the noisy environments. The widely used adaptive noise canceller (Widrow et al. 1975) consists a single stage with two microphones namely, the primary microphone which is placed close to the signal source to pick up the desired signal and the reference microphone which is placed close to the noise source to sense only the noise. Also it employs an adaptive filter with the LMS algorithm to cancel the noise component embedded in the primary signal. The adaptive filter has the task of modeling the impulse response path between noise source and primary microphone. In real-time environment, the background noise typically comes from various sources such as ventilating fan, audio equipment, engines etc. Therefore, the background noise maybe consists of wideband noise as well
Active Noise Cancellation in Audio Signal Processing
Noise cancellation of audio signal is key challenge problem in Audio Signal Processing. Since noise is random process and varying every instant of time, noise is estimated at every instant to cancel from the original signal. There are many schemes for noise cancellation but most effective scheme to accomplish noise cancellation is to use adaptive filter. Active Noise Cancellation (ANC) is achieved by introducing "antinoise" wave through an appropriate array of secondary sources. These secondary soures are interconnected through an electonic system using a specific signal processing algorithm for the particular cancellation scheme. In this paper, the three conventional adaptive algorithms; RLS(Recursive Least Square), LMS(Least Mean Square) and NLMS(Normalized Least Mean Square) for ANC are analysed based on sigle channel broadband feedforward. For obtaining faster convergence, Normalized Least Mean Square (NLMS) algorithm is modified and associated extended algorithm under...
Adaptive noise canceling for speech signals
Acoustics, Speech and Signal Processing, IEEE …, 1978
Abgtruct-A least mean-square (LMS) adaptive filtering approach has been formulated for removing the deleterious effects of additive noise on the speech signal. Unlike the classical LMS adaptive filtering scheme, the proposed method is designed to cancel out the clean speech signal. This method takes advantage of the quasi-periodic nature of the speech signal to form an estimate of the clean speech signal at time t from the value of the signal at time t minus the estimated pitch period. For additive white noise distortion, preliminary tests indicate that the method improves the perceived speech quality and increases the signalto-noise ratio (SNR) by 7 dB in a 0 dB environment. The method has also been shown to partially remove the perceived granularity of CVSD coded speech signals and to lead to an improvement in the linear prediction analysis/synthesis of noisy speech,
Advanced Digital Signal Processing and Noise Reduction
2001
ransient noise pulses differ from the short-duration impulsive noise studied in the previous chapter, in that they have a longer duration and a relatively higher proportion of low-frequency energy content, and usually occur less frequently than impulsive noise. The sources of transient noise pulses are varied, and may be electromagnetic, acoustic or due to physical defects in the recording medium. Examples of transient noise pulses include switching noise in telephony, noise pulses due to adverse radio transmission environments, noise pulses due to on/off switching of nearby electric devices, scratches and defects on damaged records, click sounds from a computer keyboard, etc. The noise pulse removal methods considered in this chapter are based on the observation that transient noise pulses can be regarded as the response of the communication channel, or the playback system, to an impulse. In this chapter, we study the characteristics of transient noise pulses and consider a template-based method, a linear predictive model and a hidden Markov model for the modelling and removal of transient noise pulses. The subject of this chapter closely follows that of Chapter 12 on impulsive noise.
An adaptive filter for noise cancelling
IEEE Transactions on Circuits and Systems, 1988
This paper introduces a new nonlinear filter that is used for adaptive noise cancelling. The derivation and convergence properties of the filter are presented. The performance, as measured by the signal to noise ratio between the signal and its estimate, is compared to that of the commonly used least mean square (LMS) algorithm. It is shown, through simulation, that the proposed nonlinear noise canceller has, on the average, better performance than the LMS canceller. The proposed adaptive noise canceller is based on Pontryagin minimum principle and the method of invariant imbedding. The computational time for the proposed method is about 10 percent that of the LMS, in the studied cases which is a substantial improvement. Key Word-Adaptive noise cancelling, Pontryagin minimum principle, invariant imbedding, nonlinear estimation, LMS algorithm, adaptive filtering.
1992
Four different classes of adaptive signal cancelers can be used to eliminate narrow-band interference from a broadband signal: (1) cascaded second-order notch filters; (2) high-order in-line notch filters; (3) second-order bandpass noise cancelers; and (4) high-order bandpass noise cancelers. Of the four, a structure based on second-order bandpass filters used as signal cancelers is found to perform better than the other structures. An adaptive algorithm for these filters has been proposed. The structure can be reduced in hardware complexity without degrading performance using a new adaptive algorithm that out-performs any of the other known structures or algorithms. This new structure is particularly suited to the elimination of narrow-band interference in broadband Bi-Phase Shift-Key (BPSK) signals with and without background noise.
Noise Cancellation Using an Adaptive Filtering Technique
Acoustic “Noise” is becoming a major problem in the field of engineering and digital signal processing. The problem that is being faced by engineers is how to decrease this noise level to a minimum or to eradicate it in total. Then came the idea of noise cancellation; a mechanism of cancelling out an unwanted noise by introducing a secondary anti-noise. To achieve this they need a special type of filter “adaptive filters”, just as the name, it is kind of filter that has the ability to adjust its position to adjust to a change in the external environment. This project is based on ways we can achieve this noise cancellation using an adaptive filter. Three adaptive filtering algorithms were implored, the LMS, RLS and Block LMS, which were all implemented both in the MATLAB and Simulink environment. A GUI was designed to allow a user to use the implemented adaptive filters, listen to an audio play back of the processing audio signals and view the processing graphics. The only shortcoming of the project is the absence of the real time workshop on the student version of MATLAB, so the GUI to synchronize with the Simulink models was displaying processing graphics in non-real time
A Novel Automatic Noise Removal Technique for Audio and Speech Signals
This paper introduces new ideas on wideband stationary/non-stationary noise removal for audio signals. Current noise reduction techniques have generally proven to be effective, yet these typically exhibit certain undesirable characteristics. Distortion and/or alteration of the audio characteristics of primary audio sound is a common problem. Also user intervention in identifying the noise profile is sometimes necessary. The proposed technique is centered on the classical Kalman filtering technique for noise removal but uses a novel architecture whereby advanced signal processing techniques are used to identify and preserve the richness of the audio spectrum. The paper also includes conceptual and derivative results on parameter estimation, a description of multi parameter Signal Activity Detector (SAD) and our new found improved results.
Detection and extraction of periodic noises in audio and biomedical signals using Kalman filter
Signal Processing, 2008
This paper studies the subject of adaptive noise cancelation using the Kalman filtering technique to achieve high precision and fast convergence. It is shown that the Kalman filter can successfully be designed to detect and extract periodic noises which may be constituted of different sinusoidal components with possibly unknown and/or time-varying frequencies. This highlights the feature of Kalman filter in synthesizing periodic noises in the time-domain which is not possible using Fourier-based methods such as DFT. Usefulness of the method is discussed in the context of two examples: active cancelation of periodic noises from audio waveforms and filtering of electrocardiogram measurements.