Normalized Least Mean Square adaptive noise cancellation filtering for speaker verification in noisy environments

Ilyas, Mohd Zaizu; Noor, Ali O. Abid; Ishak, Khairul Anuar; Hussain, Aini; Samad, Salina Abdul

doi:10.1109/iced.2008.4786632

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…This section shows the computational complexity when normalised least mean squares filter (NLMS) and recursive least squares filter (RLS) are used (Ilyas et al 2008;Cioffi and Kailath 1984;Dhiman et al 2013). These parameters will serve in the simulations carried out in the following section.…”

Section: Computational Complexity and Convergence Speedmentioning

confidence: 99%

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Performance analysis of narrowband beamforming using fully and partial adaptive beamformers with a spherical array

Torres¹,

Mateo²,

Vicente

2016

Multidim Syst Sign Process

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This research proposes an improved narrowband partial adaptive beamformer analysis using a proposed spherical array. Comparison between the fully and the partial adaptive beamformers is given. The study is performed by investigating performance parameters like the beamformer output signal-to-noise ratio and the beamformer output signal-to-interference-plus-noise ratio both in the steady state and along adaptation. Furthermore, computational complexity and convergence speed of the proposed sensor arrangement are also analyzed and examples are given. The results demonstrate that this beamformer considerably reduces the number of complex operations and features faster convergence speed.

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Section: Computational Complexity and Convergence Speedmentioning

confidence: 99%

“…Li et al (2008) suggested an element space partially adaptive array in which each sub-array uses the delay-and-sum (DAS) weights (Saff and Kuijlaars 1997). The convergence speed is enhanced in comparison to fully adaptive array and noise power is increased mildly (Ilyas et al 2008;Cioffi and Kailath 1984;Dhiman et al 2013).…”

Section: Introductionmentioning

confidence: 96%

Performance analysis of narrowband beamforming using fully and partial adaptive beamformers with a spherical array

Torres¹,

Mateo²,

Vicente

2016

Multidim Syst Sign Process

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“…The process of detecting the presence of speech/non-speech is not a fully resolved problem in speech processing systems. Numerous applications such as robust speech recognition [ 11 , 12 ], real-time speech transmission on the Internet [ 13 ], noise reduction and echo cancellation schemes in telecommunication systems are affected by such a process [ 14 , 15 ]. The detection of speech/non-speech is not an easy task as it may look.…”

Section: A Review Of Voice Activity Detection Techniquesmentioning

confidence: 99%

Development of a Voice Activity Controlled Noise Canceller

Noor

Samad

Hussain

2012

Sensors

Self Cite

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In this paper, a variable threshold voice activity detector (VAD) is developed to control the operation of a two-sensor adaptive noise canceller (ANC). The VAD prohibits the reference input of the ANC from containing some strength of actual speech signal during adaptation periods. The novelty of this approach resides in using the residual output from the noise canceller to control the decisions made by the VAD. Thresholds of full-band energy and zero-crossing features are adjusted according to the residual output of the adaptive filter. Performance evaluation of the proposed approach is quoted in terms of signal to noise ratio improvements as well mean square error (MSE) convergence of the ANC. The new approach showed an improved noise cancellation performance when tested under several types of environmental noise. Furthermore, the computational power of the adaptive process is reduced since the output of the adaptive filter is efficiently calculated only during non-speech periods.

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“…In the above equation, w stands for the output of the algorithm, n stands for iteration number, μ is a adaptation constant which controls the rate of convergence, e is the error signal and y is the reference signal. In [83], normalized LMS algorithm was used for adaptive noise cancellation and performance gain of 21% was reported at 10 dB SNR for text-dependent speaker verification.…”

Section: Speaker Verification In Environmental Noisementioning

confidence: 99%

Robust text-independent speaker verification in environmental noise

Panda¹

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Automatic speaker verification has many potential applications in security, surveillance and access control. In many of these applications, it is necessary to verify the speaker based on a short and noise degraded speech utterance. This thesis addresses the problem of robust speaker verification in environmental noise conditions by introducing novel and computationally efficient techniques that are suitable for realistic conditions. It also engenders the application of psychoacoustics to realize an adaptive model compensation technique. age, by 22% over the best performing white noise based multi-conditioning technique. Moreover, the computational complexity of the proposed multiconditioning technique is also significantly reduced due to the avoidance of compute-intensive posterior union model. Finally, the proposed techniques should pave the way for realizing speaker-aware human-computer interactions in mass volume products. Contents List of Figures viii List of Tables x Glossary xi SS Spectral Subtraction; A feature domain noise robustness technique TIMIT TI-MIT(Texas Instruments-Massachusetts Institute of Technology); A speech database with high quality clean speech UBM Universal Background Model; A background model for speaker verification xii

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Normalized Least Mean Square adaptive noise cancellation filtering for speaker verification in noisy environments

Cited by 7 publications

References 8 publications

Performance analysis of narrowband beamforming using fully and partial adaptive beamformers with a spherical array

Performance analysis of narrowband beamforming using fully and partial adaptive beamformers with a spherical array

Development of a Voice Activity Controlled Noise Canceller

Robust text-independent speaker verification in environmental noise

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