Analysis of the filtered-X LMS algorithm and a related new algorithm for active control of multitonal noise

Hinamoto, Yoichi; Sakai, Hideaki

doi:10.1109/tsa.2005.854089

Cited by 42 publications

(24 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite of the exact efficient implementations, the number of multiplications is still large compared with FxLMS. Meanwhile, we are aware of that, due to the delay of secondary path in the context of ANC, we always assume that the weight changes little in the intervals of the secondary path length, which is a reasonable assumption for sufficiently small step-size [1,[23][24][25]. Thus it is possible to utilize this regular assumption to further reduce the complexity of MFxAPSA-EEI.…”

Section: The Proposed Nfxapsamentioning

confidence: 99%

A new efficient filtered-x affine projection sign algorithm for active control of impulsive noise

Xiao

Jiang

2016

Signal Processing

View full text Add to dashboard Cite

Section: The Proposed Nfxapsamentioning

confidence: 99%

A new efficient filtered-x affine projection sign algorithm for active control of impulsive noise

Xiao

Jiang

2016

Signal Processing

View full text Add to dashboard Cite

“…It should be noted that the adaptation methods presented here for the implementation of ANC q and ANC ν are based on simple FXLMS for the sake of simplicity and quick experimentation. For the tonal active noise control part ANC q , the analysis and methods described in [9][10][11][12] can be incorporated. The proposed method can also be extended to accommodate multiple reference microphones in which case the reduced reference signal set procedure developed in [19] can lessen the controller dimension and computational complexity.…”

Section: Generation Of Antinoisementioning

confidence: 99%

“…The idea of using a parallel bank of control filters, each handling a subset of the sinusoids is presented and analyzed. The more general case of active noise control of nonharmonically related sinusiods is analysed in a novel manner in [10] by employing a characteristic equation derived from an equivalent state-space model. A faster converging version of the FXLMS algorithm is also derived.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sequentially Adapted Parallel Feedforward Active Noise Control of Noisy Sinusoidal Signals

Kannan

Panahi

Briggs

2009

Advances in Acoustics and Vibration

View full text Add to dashboard Cite

A large class of acoustic noise sources has an underlying periodic process that generates a periodic noise component, and thus their acoustic noise can in general be modeled as the sum of a periodic signal and a randomly fluctuating signal (usually a broadband background noise). Active control of periodic noise (i.e., for a mixture of sinusoids) is more effective than that of random noise. For mixtures of sinusoids in a background broadband random noise, conventional FXLMS-based single filter method does not reach the maximum achievable Noise Attenuation Level (NAL max ). In this paper, an alternative approach is taken and the idea of a parallel active noise control (ANC) architecture for cancelling mixtures of periodic and random signals is presented. The proposed ANC system separates the noise into periodic and random components and generates corresponding antinoises via separate noise cancelling filters, and tends to reach NAL max consistently. The derivation of NAL max is presented. Both the separation and noise cancellation are based on adaptive filtering. Experimental results verify the analytical development by showing superior performance of the proposed method, over the single-filter approach, for several cases of sinusoids in white noise.

show abstract

“…In another work [27] of designing ANC headset, filtered-x least means square algorithm (FXLMS), which introduces secondary path for synthesizing the reference signal, was implemented. One of the study [28] based on FXLMS analysed the algorithm and extended the new algorithm to reduce the multi-tonal noise. Similar work was developed in [29] and evaluated with narrowband and broadband noise against the instability in convergence of adaptation algorithm.…”

mentioning

confidence: 99%

Speech Enhancement Techniques: Quality vs. Intelligibility

Nemade¹,

Shah²

2014

IJFCC

View full text Add to dashboard Cite

Abstract-With the rapid advancements in industrial and technology applications, the demand from consumer for transmission and manipulation of data, primarily -speech, audio and images, are at its peak. The speech, being a fundamental way of communication for the humans, has been embedded in various essential applications like speech recognition, voice-distance-talk and other forms of personal communications. There are so many applications of speech still to be far from reality just because of lack of efficient and reliable noise removal mechanism and preserving or improving the intelligibility for the speech signals. The broad categories of speech enhancement techniques can be listed as speech filtering techniques, beam forming techniques and active noise cancellation methods. In this paper, an attempt has been stepped towards surveying the methodologies for speech improvement. It is also interesting to investigate, how these techniques affect the performance of various application systems like speech recognition and speech communication. Essentially, we also discuss about the types and sources of noise that can be considered in speech enhancements. Index Terms-Enhancement, beamforming and active noise cancellation, single channel enhancement I. INTRODUCTIONSpeech is the fundamental and common medium, hence important for us, to communicate and most effective and reliable means for expressing oneself for personal communication. With advancement in hardware technologies, there are so many electronic and mobile personal communication based devices available, today in market and that too in cheaper cost and with easy availability. The applications like speech recognition, mobile and personal communication, public address system are few of the applications from long list of speech based systems. However, undesired noises in environment like sound from heavy machines, vehicles are also present in one or other form everywhere. These noises cause undesired effects in speech transmission and acquiring systems. Recently, restricted or usable vicinity of applications is moving from one place and close room to more open and multiple locations, leading to several of types undesired signals of mixing with desired speech signal making speech more corrupt with noise. Not only human communications but intelligent machines which Manuscript received September 14, 2013; revised November 12, 2013 trying to automate the things and sometimes also takes decision based on what it receives as a speech, also suffers from the degraded performance.Since last five decades, various approaches for noise reduction and speech enhancements have been investigated and developed. Among, very early and fundamental approach of noise reduction was introduced to use the theory of the optimum Wiener filter. Given a desired signal and an input signal, the Wiener filter produces an estimate of the desired signal that is optimal, i.e. the squared mean error or difference between the signals is minimized. The Wiener filter can also be adaptively estimated us...

show abstract

Analysis of the filtered-X LMS algorithm and a related new algorithm for active control of multitonal noise

Cited by 42 publications

References 7 publications

A new efficient filtered-x affine projection sign algorithm for active control of impulsive noise

A new efficient filtered-x affine projection sign algorithm for active control of impulsive noise

Sequentially Adapted Parallel Feedforward Active Noise Control of Noisy Sinusoidal Signals

Speech Enhancement Techniques: Quality vs. Intelligibility

Contact Info

Product

Resources

About