Hardware implementation of adaptive filter for noise cancellation using TMS320C6713

Swati S Godbole1, Sanjay B Pokle2

(1. G.H.Raisoni College of  Engineering, Rashtrasant Turadoji Maharaj Nagpur University, Nagpur 440016, India;2. Shree Ramdevbaba College of Engineering and Management, Rashtrasant Turadoji Maharaj Nagpur University, Nagpur 440016, India)

Abstract： Daily, we experience the effects of audio noise, which contaminates the original information bearing signal with noise from its surrounding environment. This paper focuses on real-time hardware implementation of multi-tap adaptive noise cancellation (ANC) system by using the least mean square (LMS) algorithm on TMS320C6713 to remove undesired noise from a received signal for various audio related applications. Three different experiments are carried out by considering different audio inputs to test the efficiency of the designed ANC system. The ‘C’ code implementation of LMS algorithm is introduced and simulated in code composer studio (CCS), then realized on the digital signal processor (DSP) C6713. The 300 Hz, 500 Hz, 800 Hz, 1 kHz and 3 kHz of tone signals and male speech signal are used as the reference inputs to trace the noise of signal until it is eliminated. The performance of ANC system is studied in terms of convergence speed, order of the filter and signal-to-noise ratio (SNR). The experimental results demonstrate that the designed system shows a considerable improvement in SNR. 

Key words： adaptive noise cancellation (ANC); digital signal processor (DSP); mean square error (MSE); least mean square algorithm (LMS); TMS320C6713 DSK; code composer studio (CCS); signal-to-noise ratio (SNR)

CLD number： TN911.7 Document code： A

Article ID： 1674-8042(2014)03-0038-010   doi： 0Introduction

References

[1] Haykin S  O. Adaptive filter theory. Upper Saddle River: Prentice Hall, 2002.
[2] Widrow  B,  Glover J R,  McCool J M, et al. Adaptive noise cancelling: principles and applications. In: Proceedings of the IEEE, 1975, 63(12): 1692-1716.
[3] Rosado-Muoz A, Bataller-Mompeán M,  Soria-Olivas E, et al. FPGA implementation of an adaptive filter robust to impulsive noise: two approaches. IEEE Transactions on Industrial Electronics, 2011, 58(3): 860-870.
[4] Jheng H Y, Chen Y H, Ruan S J, et al. FPGA implementation of high sampling rate in-car non-stationary noise cancellation based on adaptive Wiener filter. In: Proceedings of 2011 IEEE/IFIP 19th International Conference on VLSI and System-on-Chip (VLSI-SoC), Hong Kong, 2011: 114-117.
[5] Texas Instruments, Inc. Code composer studio user’s guide, No. SPRU32, 1999.
[6] Texas Instruments, Inc. Code composer studio quick start guide, No. SPRU368A, 1999.
[7] Martínez M E I.  Implementation of QRD-RLS algorithm on FPGA∥Application to noise canceller system. IEEE Latin America Transactions, 2011, 9(4): 458-462.
[8] Nekouei F, Zargar-Talebi N,  Kavian Y S, et al. FPGA implementation of LMS self correcting adaptive filter (SCAF) and hardware analysis. In: Proceedings of the 8th IEEE, IET  International Symposium on Communication Systems, Networks and Digital Signal Processing, Poznan, 2012: 1-5.
[9] EdGate Technologies. TMS320C6713 DSK user manual. [2014-03-11]. http:∥c6000.spectrumdigital.com/dsk6713/V2/docs/dsk6713TechRef.pdf.
[10] TMS320C6713x DSP library programmer’s reference. [2014-03-21]. http:∥www.s3.kth.se/signal/projectcourse/2005/yellow/.../TI.../spru657.pdf
[11] Wallace R B, Goubranand R A. Noise cancellation using parallel adaptive filters. IEEE Transactions on Circuits and Systems-11: Analog and Digital Signal Processing, 1992, 39(4): 239-243.
[12] Mollaei Y. Hardware implementation of adaptive filters. In: Proceedings of 2009 Student Conference on Research and Development (SCOReD), UPM Serdang, Malaysia, 2009: 45-48.

基于 TMS320C6713 的去噪自适应滤波器硬件实现

Swati S Godbole1, Sanjay B Pokle2

(1. G.H.Raisoni College of  Engineering, Rashtrasant Turadoji Maharaj Nagpur University, Nagpur 440016, India;2. Shree Ramdevbaba College of Engineering and Management, Rashtrasant Turadoji Maharaj Nagpur University, Nagpur 440016, India)

摘要:我们每天都会受到音频噪音的影响， 原始信息会受到周围环境噪声信号的污染。 为此， 本文研究了一个多抽头自适应去噪实时硬件系统， 它利用 TMS320C6713 上实现的最小均方算法(LMS)来去除与音频相关的应用程序中接收到的不期望出现的噪声信号。 文章首先介绍了最小均方算法的C语言实现并在Code Composer Studio上进行仿真， 最后在C6713上实现。 考虑不同的音频输入， 进行了三项实验来测试所设计的自适应去噪系统的效率。 实验采用300、500、800、1 000 和3 000 Hz的音频信号及男性语音信号为输入的参考信号， 持续检测信号中的噪声， 直至将它全部去除。 此外， 还研究了与自适应去噪系统性能相关的收敛速度、 滤波器安排顺序以及信噪比。 实验结果表明， 所设计系统其信噪比有很大的改善。

关键词:自适应去噪（ANC）；   数字信号处理器（DSP）；  均方误差（MSE）；  最小均方算法（LMS）；   TMS320C6713 DSK；  信号调试器（CCS）；  信噪比(SNR)

引用格式：Godbole S S, Pokle S B. Hardware implementation of adaptive filter for noise cancellation using TMS320C6713. Journal of Measurement Science and Instrumentation, 2014, 5(3): 38-47. [doi: 10.3969/j.issn.1674-8042.2014.03.008]

[full text view]