DSP implementation of the FxLMS algorithm for active noise control: Texas instruments TSM320C6713DSK

García, Mauricio Hernández; Patino, Diego; Quintana, Ricardo

doi:10.1109/ccac.2015.7345188

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…In the environment, the desired sound signals are mixed with the unwanted signals (noise) [5]. Figure 7 shows the acquisition of the desired signal and the mixed noisy signal.…”

Section: Results Of the Implementationmentioning

confidence: 99%

Attenuation of Environmental Noise through Digital Filtering

Ríos¹,

Aguilar²

2021

Noise and Environment

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This chapter proposes the implementation of an environmental noise cancelation system using the least mean squares algorithm with noise amplitude modulation (NAMLMS). The system was implemented in a dsPIC3020F10 digital processor. The results obtained demonstrate that low- and high-frequency signals are attenuated allowing the passage of an audible range between 5 kHz and 18.9 kHz, the above using real-time processing.

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“…In the environment, the desired sound signals are mixed with the unwanted signals (noise) [5]. Figure 7 shows the acquisition of the desired signal and the mixed noisy signal.…”

Section: Results Of the Implementationmentioning

confidence: 99%

Attenuation of Environmental Noise through Digital Filtering

Ríos¹,

Aguilar²

2021

Noise and Environment

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“…After arithmetic operation in the controller, a series of sound wave signals opposite to the source noise are calculated. The calculated signal is transmitted into the speaker and played out, the purpose is to eliminate the source noise and achieve the effect of noise reduction [16].…”

Section: Simulation Analysismentioning

confidence: 99%

A new online secondary path modeling method with white noise amplitude adjustment for excavator cab

Long

Zhao

Liu

2022

J. Phys.: Conf. Ser.

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This article describes a new active noise control system to reduce the noise in the excavator cab. The noise in the excavator cab is mainly low-frequency noise. A digital signal processor (DSP) is selected to control the microphone and speaker for active noise control. In the traditional online secondary path modeling method, the amplitude of the white noise is fixed, and the amplitude of the white noise is still large after the modeling is completed, which will affect the reference microphone. In response to the above problems, I made improvements in the traditional algorithm and added a white noise amplitude adjustment module. After the iterative algorithm of the identification filter converges, the amplitude of the white noise is reduced. In addition, when the secondary path is disturbed, the amplitude of the white noise is increased in order to remodel the secondary path. That is to say, the white noise amplitude adjustment is based on the first-order forward difference of the square value of the error signal every 50 times. What’s more, the iterative algorithm of the control filter and the identification filter adopts the idea of variable step size, which can speed up the convergence of the algorithm. Finally, the feasibility of the algorithm is verified through matlab simulation and experiment. In the simulation, it can achieve a noise reduction of about 11dB. In the experiment, a noise reduction of 5dB can be achieved, and a noise reduction of 9dB can be achieved at the frequency with the largest noise amplitude. In a word, the proposed algorithm is superior to traditional online secondary path modeling algorithm.

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“…Algunos de los algoritmos más utilizados por su baja complejidad computacional son el algoritmo de mínimos cuadrados promediados (Least Mean Square-LMS) y su versión normalizada (Normalized Least Mean Square-NLMS). Dichos algoritmos han sido implementados con éxito en diferentes trabajos de sistemas CAR [3][4][5][6], sin embargo, como se mencionó anteriormente la velocidad de convergencia de los algoritmos utilizados suele ser lenta. En [7] se presenta un sistema CAR en el que utiliza el algoritmo de proyecciones afines (Affine Projection-AP), el cual presenta una velocidad de convergencia más rápida que la de los algoritmos LMS, sin embargo, su complejidad computacional es bastante elevada ya que para el cálculo de coeficientes se requiere realizar diversas operaciones de inversión de matrices, esto provoca que su implementación en hardware sea muy complicada.…”

Section: Introductionunclassified

Implementación de un cancelador activo de ruido en un ducto usando el algoritmo FX-ECLMS

Pichardo¹,

Maya²,

Vázquez³

et al. 2017

RCS

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En este artículo se presenta la implementación de un sistema de cancelación activa de ruido en un ducto usando el algoritmo de mínimos cuadrados promediados con error codificado (Error Coded Least Mean Square-ECLMS). Para reducir los efectos producidos por la trayectoria secundaria se utilizó la estructura de filtrado-X. El sistema fue implementado en el procesador digital de señales TMS320C6713 de Texas Instruments. Los resultados experimentales demuestran que al usar el algoritmo ECLMS se logra cancelar de manera adecuada señales de baja frecuencia, además, la carga computacional se reduce considerablemente. Palabras clave: control activo de ruido, filtrado adaptativo, algoritmo ECLMS.

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DSP implementation of the FxLMS algorithm for active noise control: Texas instruments TSM320C6713DSK

Cited by 5 publications

References 10 publications

Attenuation of Environmental Noise through Digital Filtering

Attenuation of Environmental Noise through Digital Filtering

A new online secondary path modeling method with white noise amplitude adjustment for excavator cab

Implementación de un cancelador activo de ruido en un ducto usando el algoritmo FX-ECLMS

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