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Research Article

Implementation of Active Noise Filter for Real-time Noise Reduction Using the TMS320C5402 DSP Kit

S. Manikandan and M. Madheswaran
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Noise detection and reduction has become a primary concern in the fields where high speed and reliable communication is the essential criterion. It is therefore necessary to develop and build certain real time components, which can be made to work under the conditions of real-time environment where reliability and speed should be very high. In this field of Real-Time Digital Signal Processing numerous processors have been made by various companies like Texas Instrumentation, Motorola, Intel, etc. These devices offer an agreeable solution for this application. A comparable study has been made between some of the kits namely TMS320C5402 made by Texas Instrumentation. For the purpose of analysis a fundamental Active Noise filter design has been used as a test program and results are obtained from real time working conditions.

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  How to cite this article:

S. Manikandan and M. Madheswaran, 2007. Implementation of Active Noise Filter for Real-time Noise Reduction Using the TMS320C5402 DSP Kit. Journal of Applied Sciences, 7: 2750-2754.

DOI: 10.3923/jas.2007.2750.2754



Adaptive Signal Processing is a method to cancel the undesired ambient noise by adding a secondary sound wave with the same amplitude and the reverse phase to the original signal. A computer processing electrically produces such secondary sound. This technique is effectively performed for the low or middle frequency sound waves (Elliott and Nelson, 1993). An adaptive filter is composed of the Finite Impulse Response (FIR) filter as a digital filter and the Least Mean Square (LMS) algorithm as adaptive control algorithm, these criterion apparently imposes certain restrictions on the canceling system. Firstly, for highly effective noise canceling system the noise source must be nearly stationary in relation to the speaker emitting the anti-noise waveform. Second, the noise source should be located in close proximity to the noise filter. Acoustic delay is another important issue that must be dealt with in a noise cancellation system. Physically there is always a distance between the source, the anti-noise generator and the residue noise detector. These physical distances provide noise propagation delays, which in turn cause different phase shifts, depending on the relative location of objects.


Our aim is to achieve noise reduction for signals transmitted through the wireless medium. In such a communication, all the noise is added in the channel. The noise is highly random. Here there is no source for obtaining a correlated noise at the receiving end. (Morinushi, 1991). Only the received signal can tell the story of the noise added to it. Hence somehow, only if it is possible extract the noise from the received signal, through some means, then the above-mentioned adaptive techniques to enhance the signal to noise ratio of the received signal (Takahashi and Hamada, 1991). A method to obtain a correlated noise from the received signal.


For the sake of simplicity both noise and anti-noise waveforms within the same vicinity are assumed. The basic mathematics involved in building an Adaptive Noise Filter is listed below:




e (n): corresponds to the error generated because of the combined effect of the noise signal and the antinoise waveform.

w(n): corresponds to the correction factor that corresponds to the proportion of error has to be applied to the existing anti-noise waveform (Fig. 1).

y(n): is the anti-noise waveform that is applied (Tohma, 1991).

During this process of noise reduction there is a delay factor that has to be taken into account. The delay corresponds to the sum of propagation delay and processing delay. Propagation delay is the time taken for the signal to reach the processor from the external transducer, which is the micro-phone (Takahashi and Abe, 1993). Similarly a time delay has to be taken into account of the processing speed. The total delay serves as a Fig. 2 of merit for the system.

The process should be optimized such that the noise signal is reduced to a considerable extent without creating any additional disturbance in the system (Eriksson, 1993; Widrow and Stearns, 1985). The delay corresponding to the propagation of the sound is fixed for a given setup and is solely dependant on the velocity of sound in that medium, which is a constant. The factor that can be altered is the processing delay. The basic constraint of the Active Noise Filter is its inability to be used as a noise reduction system for high frequency components. In the likelihood’ of this system being used for filtering high frequency components, the delay time is significant to that of the signal and can introduce more amount of noise.

During simulation of the circuit points of singularities were created when delay time became significantly larger than time period of the frequency component.

Fig. 1: Simple active noise detection model

This will lead to creation of additional noise and the system fails to perform.

In this critical period the algorithm for ANF has to be processed and sent to the antinoise generator. The processing delay mentioned above is dependant of the processor and the optimization of the algorithm. Keeping the same algorithm, the choice of processor solely determines the delay time and correspondingly fixes an upper limit on the maximum frequency that could be detected and reduced by the setup (Haykin, 1984).


The complexity of an adaptive filter is usually measured in terms of its multiplication rate and storage equipment. The data flow and handling considerations are also major factors due to parallel hardware multiplier, pipeline architecture and the size limitation of the fast on-chip memory.

Implementation should be made more efficient by taking advantage of these attributes in the DSP's architecture. The TMS3220C25 can execute an instruction in as little as 8i0ns and the processors architecture makes it possible to execute more than one operation per instruction cycle (Casali and Robinson, 1994). In order to produce the fastest filtering routine, all data buffer memories and filter coefficients are stored in data random access memory.

The two models, which were used to test the filter, are Floating Point Arithmetic and Fixed Point Arithmetic. A brief comparison about the two is given below:

In terms of economy the Floating Point Arithmetic processor is more expensive than the Fixed Point Arithmetic. The amount of heat generated also is significant in the case of the former
In terms of precisions the Floating Point Arithmetic processor uses the IEEE 754 standard of representation of floating point numbers in terms of mantissa and exponent (Nishiyama et al., 1979). All arithmetic operations retain their precession during the operation.

Fig. 2: Critical issues in the design of an Active Noise Filter

Fig. 3: Simulation result for Error signal

Fig. 4: Simulation result for Adaptive Filter using a delay time of two counts

Fig. 5a, b: Noise signal in time domain and noise power spectrum at an error microphone

The C Code that was implemented in the processor is put as an Appendix. The code is relatively larger and optimization should be done very carefully. For the sake of simplicity Code Conversion Studio was used and the code is placed in Appendix.


The algorithm for the simulation of the Active Noise Filter was originally done in Matlab 6.5 (Fig. 3-5). The code was rewritten in standard C format for easy conversion to assembly language. (Sherratt et al., 1999). The Matlab source code that was originally used to test the system is listed in Table 1.

Table 1: Total noise reduction in the Error Microphone


The Active Noise Filter was embedded as a programming the DSP kit TMS320C5402 and the results were found to be similar to the results generated by Matlab 6.5. The real time application of the Active Noise filter was successful. Modern day DSP Kits like TMS320C5402, TMS 320C6211 are fast enough to provide the speed and reliability of real-time Noise filtering. The significance of this DSP Kit shows the major advancements in the field of signal processing which will have a dramatic impact on Real-Time processing is done Compact devices embedded with TMS processors installed can be used as effective communication equipments. Finally the DSP processor kit was more accurate as compare to Matlab6.5 software for real-time process.



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