HomeInnovatusvol. 3 no. 1 (2020)


Allan Godfrey S Sagun | Celso Co | Bartolome T Tanguilig III



This study focuses on Active Noise Reduction Using Discrete Wavelet Transform. Noises emanating from electric drill, pneumatic system, bearing of rotating motors and many others, can be classified as non-stationary signals. These examples of noises typically coming from machine shops contributes to the interference of desired signals. The objective of this study is to provide a system that simulates how to reduce unwanted signal generated by electrical system noise. In order to perform this task, a recorded voice and noise were processed by wavelet transform to get the wavelet coefficients of signals. In this research, detail and approximation coefficient values were used for filtration algorithm. The algorithm was then customized to specific voice characteristic. For the reduction of noise, a Discrete Wavelet Transform (DWT) was applied for signal processing. Different wavelet such as Daubechies Wavelet Transform Db02, Db04 and Db08 however were used to test its effectiveness. Signal-to-noise ratio formula was used to validate if the reconstructed signal is identical with the uttered voice. Graphical programming Language of Laboratory Virtual Instrumentation Engineering Workbench (LABVIEW) was utilized to manage a number of simulation experiments for validation. Industrial power tool producing noise such as grinder, power drill, single phase motor, vacuum cleaner and welding machine were used as a source of generated noise. In the reconstruction of filtered output, Inverse Wavelet Transform was utilized for the reference-desired signal to verify the algorithm for noise cancellation. It is found that DB02 provides better results for the combination of voice-vacuum cleaner and voice-welding machine, DB04 is better for voice-grinder combination while DB08 had better noise reduction to voice-power drill and voice –single phase motor.