Advances in Signal Processing 4(1): 1-5, 2016 http://www.hrpub.org DOI: 10.13189/asp.2016.040101 Increasing the Accuracy of Reverberation Time Measurement Using Wavelet Thresholding Denny Hermawato 1,* , Hastuadi Harsa 2 1 Acoustic & Vibration Metrology Laboratory, Research Center for Metrology – Indonesian Institute of Sciences (RCM-LIPI), Indonesia 2 Research and Development Centre, BMKG Jakarta, Indonesia Copyright©2016 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Abstract Reverberation time measurement is used in building acoustic analysis and determination of acoustic characteristic of a material such as sound transmission loss and sound absorption index. This process measures the time needed for sound to decay 60 dB since the steady sound in the room is switched off. Usually, the reverberation time is estimated from sound decay curve. In the real reverberation time measurement, the smooth sound decay curve is hard to find because the original sound wave combines with reflection sound wave. In this paper, two level Daubechies wavelet transform is performed to filter noise in the sound decay. The applied wavelet thresholding successfully filtered out the maximum and minimum peak in the sound decay curve, therefore the estimation of reverberation time can be done more accurately in high and low frequency region. Keywords Sound Measurement, Reverberation Time, Wavelet Filter, Daubechies 1. Introduction Reverberation time is defined as the time in seconds required by sound level to decay 60 dB after a sound source abruptly switch-off [1]. It is used in the sound transmission loss test to determine the STC (Sound Transmission Class) value of partition panel test material [2]. It is also used in absorption coefficient measurement to determine the value of NRC (Noise Reduction Index) of an absorber material [2]. The conventional reverberation time measurement employs many equipment thus make it inefficient for field measurement. The decay of sound level is printed on paper sheet and the value of reverberation time is determined manually using a tool called protractor that convert the sound level decrement slope into time. The determination of reverberation time would spend a lot of time because it is done one by one in 1/3 octave from frequency 125 until 4000 Hz. In the modern technology, the measurement of reverberation time can be done by utilizing analyzer which has spectrum recording facilities [3]. The 60 dB decrement is estimated directly and simultaneously from the recorded spectrum. Estimation of 60 dB decrement in high frequencies, e.g. above 2500 Hz can be performed easily because the sound decay is nearly linear. But in low frequency region, the decay is not linear; this is because the original sound decay is distorted by reflection sound from the wall. At the microphone, the original sound and reflection sound are summing out together, if the original signal is in phase with reflection signal then the resultant sound level will be higher and if they are out of phase then the resultant signal will be lower than original signal. The resultant signal will create maximum and minimum peak in the sound spectrum makes it hard to estimate the sound decay. Therefore, a post processing to the signal needs to be employed to enhance the accuracy of reverberation time estimation in low frequencies. Wavelet has been successfully applied in various research areas, such as acoustical signal processing, power production and power electronics, non-destructive testing, chemical processing, stochastic signal processing, image compression, satellite imagery, machine vision, bioinformatic and flow analysis [4]. Donoho [5] proposed a powerful approach for noise reduction called wavelet thresholding and its main applications is for denoising data and images [6, 7, 8]. This paper proposes the application of wavelet thresholding method in the field of acoustic measurement focused on reverberation time measurement. 2. Wavelet Thresholding Methods Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale [9]. A wavelet is a kernel function used in an integral transform [4]. The wavelet function (CWT) of a continuous signal x(t) is given by: () () () ∫ ∞ ∞ − + b a, b a, dt t t x = x W (1) with the wavelet function defined by dilating and translating a mother function as: