Active sound profiling of narrowband signals for improving sound quality in an enclosed space

Ryu, Seokhoon; Kim, Jeakwan; Lee, Young-Sup

doi:10.1007/s11042-017-4648-3

Cited by 10 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…16 For example, Thilagam proposed an active interior noise control system based on the FxLMS algorithm, which was simulated in MATLAB, and the computational results showed a significant reduction in the sound pressure level 17 ; Kim et al adapted a step-size and normalized LMS (NLMS) algorithm to quickly train secondary path filters 18 ; Wang and Zhang combined auditory masking effect and normalized frequency-domain block into FxLMS algorithm, and verified its advantages of fast convergence and small error through vehicle steady-state noise test and data analysis [8][9][10] ; With the help of FxLMS algorithm, Ryu and others carried out experimental research on noise reduction and sound quality improvement of rotating machinery in an enclosed space. 19 To sum up, ANC has achieved certain results in some areas such as automobiles, but no reports in buses. In this paper, the LMS, NLMS and FxLMS algorithms are utilized to explore the ANC models suitable for an electric bus driving position, and numerical simulations are conducted to evaluate the noise reduction performance and its influence on sound quality.…”

Section: Introductionmentioning

confidence: 99%

Active noise control modeling for an electric bus driving position and its effect on sound quality

Zhang,

Peng,

Chen

et al. 2024

Noise & Vibration Worldwide

View full text Add to dashboard Cite

Current electric bus has become an important public transport for people in China, and its interior sound quality directly affects the driver’s physical and mental health for a long working time and the comprehensive performance of the vehicle. Therefore, it is an inevitable and urgent trend to research electric bus interior sound quality. Aiming at the problem of subjective uncomfortable auditory sensation caused by different speeds, the noise signals near the driver’s ears were collected under two constant speed conditions. On the basis of principle analysis of feed-forward adaptive control system, the primary and secondary path transfer functions were presented using Simulink module in MATLAB to construct three active noise control models for an electric bus based on least mean square (LMS), normalized LMS (NLMS) and filtered-x LMS (FxLMS). Numerical simulation results indicate that the established control models are convergent, and four acoustic parameters are effectively reduced after controls to achieve the effect of improving the sound quality.

show abstract

Section: Introductionmentioning

confidence: 99%

Active noise control modeling for an electric bus driving position and its effect on sound quality

Zhang,

Peng,

Chen

et al. 2024

Noise & Vibration Worldwide

View full text Add to dashboard Cite

show abstract

“…These periodic components are related to the rotational speed [1] of the mechanical device and are usually very annoying. Therefore, systems that can cancel them, or at least minimize their sound level [2]- [9] or their perceived annoyance [10]- [14], or even equalize the noise [15]- [20], are required.…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

Ferrer¹,

Diego²,

Hassani

et al. 2022

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

In this work we propose a multi-channel active noise equalizer (ANE) that can deal with multi-frequency noise signals and assigns simultaneously different equalization gains to each frequency component at each monitoring sensor. For this purpose, we state a pseudo-error noise signal for each sensor of the ANE, which has to be cancelled out in order to get the desired equalization profiles. Firstly the optimal analytic solution for the ANE filters in the case of single-frequency noise is provided, and an adaptive algorithm based on the Least Mean Squared (LMS) is proposed for the same case. We also show that this adaptive strategy reaches the theoretical solution in steady state. Secondly, we state an equivalent approach for the case of multi-frequency noise based on two alternatives: a common pseudo-error signal at each sensor for all frequencies, and a different pseudo-error signal at each sensor for each frequency. The analytic and adaptive solutions for the ANE control filters have been developed for both pseudo-error alternatives. Finally, the ability of the proposed ANE to achieve simultaneously different user-selected noise profiles in different locations has been validated by their transfer functions and simulations.

show abstract

“…Moreover, the SPLs of some engine orders which are insufficient also need to be enriched at the same time. These have led to the studies of active sound profiling algorithms which are also known as active sound quality control (ASQC) to adjust the SPL of each engine order to a certain target level 4‐8 . These algorithms were devised to reduce, enrich, or maintain the SPL of engine noise to a pre‐defined level.…”

Section: Introductionmentioning

confidence: 99%

Effect of virtual error sensor location for active sound quality control in a car cabin

Ryu

Kim

Lee

2020

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Summary A virtual error microphone‐based active sound quality control (VEM‐ASQC) algorithm is proposed to make engine sound quality better inside a car cabin in this study. In a single‐input and single‐output system, if a VEM is installed at either left or right ear, the sound pressure level (SPL) at the other ear can be fluctuated, and this leads the excessive SPL difference between the two ears when previous methods were applied. In this paper, an uncomplicated method that can make engine sounds at both left and right ears track the target engine sound levels with less errors and reduces the left‐right tracking error (TE) (in SPL) difference was investigated. The suggested VEM‐ASQC algorithm was implemented in an actual car for real‐time control experiment, and target profiles of nine engine orders were designed to control the sound quality actively. The control experiment showed that the proposed approach reduces the errors to track the target levels and the left‐right TE differences more compared to those by the previous methods.

show abstract

Active sound profiling of narrowband signals for improving sound quality in an enclosed space

Cited by 10 publications

References 6 publications

Active noise control modeling for an electric bus driving position and its effect on sound quality

Active noise control modeling for an electric bus driving position and its effect on sound quality

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

Effect of virtual error sensor location for active sound quality control in a car cabin

Contact Info

Product

Resources

About