Psychoacoustic hybrid active noise control system

Wang, Tongwei; Gan, Woon‐Seng; Chong, Yong Kim

doi:10.1109/icassp.2012.6287881

Cited by 7 publications

(19 citation statements)

References 7 publications

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“…In [5], a pre-emphasis filter was roughly generated by applying A-weighting [12] which mimics the acoustic frequency gain function (AFGF) of human hearing. In this paper, we aim to derive the second pre-emphasis filter to account for the SMGF of human hearing.…”

Section: Smgf-weighted Pre-emphasis Filtermentioning

confidence: 99%

“…Finally, a smoothed version of the averaged profile was adopted as the second pre-emphasis filter as shown in Figure 3. Figure 4 shows the block diagram of the psychoacoustic hybrid ANC system in [5], which combines the FELMS algorithm with the typical hybrid ANC system. In this paper, we extend this block diagram by adding the second preemphasis filter to take the SMGF of human hearing into account.…”

Section: Smgf-weighted Pre-emphasis Filtermentioning

confidence: 99%

“…In this paper, we extend this block diagram by adding the second preemphasis filter to take the SMGF of human hearing into account. The update rules of the coefficients of the two adaptive filters W 1 (z) and W 2 (z) can be directly extended from [5] as:…”

Section: Smgf-weighted Pre-emphasis Filtermentioning

confidence: 99%

“…The filtered-error least-mean-square (FELMS) algorithm, which incorporates different noise weights to shape the spectrum of residual error, was proposed in [3] and motivated the development of psychoacoustic ANC systems. As a result, psychoacoustic ANC systems, which consider the hearing thresholds at different acoustic frequencies, were proposed in the FELMS framework over the past few years [4] [5].…”

Section: Introductionmentioning

confidence: 99%

“…The concept of considering different hearing thresholds of acoustic frequencies in developing psychoacoustic ANC systems [4][5] is extended to include different detection thresholds of spectral modulations. We extend the hybrid ANC system in [5] by further incorporating another pre-emphasis filter, which accounts for the spectral modulation sensitivity of human hearing.…”

Section: Introductionmentioning

confidence: 99%

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A Spectral Modulation Sensitivity Weighted Pre-Emphasis Filter for Active Noise Control System

Cheong¹,

Wang²,

Chi³

2016

Interspeech 2016

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Psychoacoustic active noise control (ANC) systems by considering human hearing thresholds in different frequency bands were developed in the past. Besides the frequency sensitivity, human hearing also shows different sensitivity to spectral and temporal modulations of the sound. In this paper, we propose a new psychoacoustic active noise control system by further considering the spectral modulation sensitivity of human hearing. In addition to the sound pressure level (SPL), the loudness level is also objectively assessed to evaluate the noise reduction performance of the proposed ANC system. Simulation results demonstrate the proposed system outperforms two compared systems under test conditions of narrowband and broadband noise in terms of the loudness level. The proposed algorithm has been validated on TI C6713 DSP platform for real-time process.

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Section: Smgf-weighted Pre-emphasis Filtermentioning

confidence: 99%

Section: Smgf-weighted Pre-emphasis Filtermentioning

confidence: 99%

Section: Smgf-weighted Pre-emphasis Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Spectral Modulation Sensitivity Weighted Pre-Emphasis Filter for Active Noise Control System

Cheong¹,

Wang²,

Chi³

2016

Interspeech 2016

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A survey on active noise control in the past decade—Part I: Linear systems

et al. 2021

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Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems. Since its first introduction in 1936, this approach has been greatly developed. This paper focuses on discussing the development of ANC techniques over the past decade. Linear ANC algorithms, including the celebrated filtered-x least-mean-square (FxLMS)based algorithms and distributed ANC algorithms, are investigated and evaluated. Nonlinear ANC (NLANC) techniques, such as functional link artificial neural network (FLANN)-based algorithms, are pursued in Part II. Furthermore, some novel methods and applications of ANC emerging in the past decade are summarized. Finally, future research challenges regarding the ANC technique are discussed.

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