Noise Cancellation Employing Adaptive Digital Filters for Mobile Applications

The persistent improvement of the hybrid adaptive algorithms and the swift growth of signal processing chip enhanced the performance of signal processing technique exalted mobile telecommunication systems. The proposed Artificial Neural Network Hybrid Back Propagation Adaptive Algorithm (ANNHBPAA) for mobile applications exploits relationship among the pure speech signal and noise corrupted signal in order to estimate of the noise. An adaptive linear system responds for changes in its environment as it is operating. Linear networks are gets adjusted at each time step based on new input and target vectors can find weights and biases that minimize the networks sum squared error for recent input and target vectors. Networks of this kind are quite oftenly used for error cancellation, speech signal processing and control systems.    Noise in an audio signal has become major problem and hence mobile communication systems are demanding noise-free signal. In order to achieve noise-free signal various research communities have provided significant techniques. Adaptive noise cancellation (ANC) is a kind of technique which helps in estimation of un-wanted signal and removes them from corrupted signal. This paper introduces an Adaptive Filter Based Noise Cancellation System (AFNCS) that incorporates a hybrid back propagation learning for the adaptive noise cancellation in mobile applications. An extensive study has been made to explore the effects of different parameters, such as number of samples, number of filter coefficients, step size and noise level at the input on the performance of the adaptive noise cancelling system. The proposed hybrid algorithm consists all the significant features of Gradient Adaptive Lattice (GAL) and Least Mean Square (LMS) algorithms. The performance analysis of the method is performed by considering convergence complexity and bit error rate (BER) parameters along with performance analyzed with varying some parameters such as number of filter coefficients, step size, number of samples and input noise level. The outcomes suggest the errors are reduced significantly when the numbers of epochs are increased. Also incorporation of less hidden layers resulted in negligible computational delay along with effective utilization of memory. All the results have been obtained using computer simulations built on MATLAB platfor

Pengurangan Noise Pada RTL-SDR Menggunakan Least Mean Square Dan Recursive Least Square

Noise reduction is an important process in a communication system, one of which is radio communication. In the process of broadcasting radio Frequency Modulation (FM) often encountered noise so that listeners find it difficult to understand the information provided. In the past, noise reduction used traditional filters that were only able to filter certain frequencies. However, for future technologies an adaptive filter is needed that can dynamically reduce noise effectively. Register Level-Software Defined Radio (RTL-SDR) can capture signals with a very wide frequency range but has a less clear sound quality. So it needs to be done noise reduction. In this study, two methods are used, namely Least Mean Square (LMS) and Recursive Least Square (RLS). The data used five radio stations in Malang. The results showed that the LMS algorithm is stable but has a slow convergence speed, whereas the RLS algorithm has poor stability but has a high convergence speed. From the test, it can be concluded that the performance of RLS is better than LMS for noise reduction in RTL-SDR. The best performance is the reduction of White Noise using RLS on the Oryza radio station with an Normalized Weight Differences (NWD) value of -13.93 dB.Pengurangan noise merupakan proses penting dalam suatu sistem komunikasi, salah satunya pada komunikasi radio. Pada proses broadcasting radio Frequency Modulation (FM) sering dijumpai noise sehingga pendengar sulit memahami informasi yang diberikan. Dulu, pengurangan noise menggunakan filter tradisional yang hanya mampu memfilter frekuensi tertentu. Namun, untuk teknologi yang akan datang diperlukan penggunaan filter adaptif yang secara dinamis dapat efektif mengurangi noise. Register Transfer Level-Software Defined Radio (RTL-SDR) bisa menangkap sinyal dengan range frekuensi yang sangat luas namun mempunyai kualitas suara yang kurang jernih. Sehingga perlu dilakukan pengurangan  noise. Pada penelitian ini digunakan dua metode yaitu Least Mean Square (LMS) dan Recursive Least Square (RLS). Data yang digunakan dalam penelitian adalah lima stasiun radio yang ada di Malang. Hasil penelitian menunjukkan bahwa algoritma LMS ini stabil namun memiliki kecepatan konvergensi yang lambat, sedangkan pada algoritma RLS memiliki kestabilan yang kurang baik namun memiliki kecepatan konvergensi yang tinggi. Dari pengujian dapat disimpulkan bahwa kinerja RLS lebih baik daripada LMS untuk pengurangan noise pada RTL-SDR. Kinerja terbaik yaitu pengurangan White Noise menggunakan RLS pada stasiun radio Oryza dengan nilai Normalized Weight Differences (NWD) -13.93 dB