3 Experimental Setup 3.1 Participants
3.3 Data Analysis
3.3.1 Preprocessing
The data preprocessing by centering. This preprocessing aims to simplify the ICA algorithm.
Preprocessing process is by subtracting the average of the observed data [3]. Without presprocessing iteration process can achieve 500 times and can not be determined value unmixing matrix. By preprocessing process is done, the iteration to 6 times the value of unmixing matrix A.
A = .
3.3.2 ICA
ICA process is done in two stages, by making their own algorithms and use fastICA 2.5 [5].
fastICA 2.5 is a MATLAB-based ICA tool that includes a number of analysis and visualization techniques set in a user-friendly graphical interface [6].
Based on previous studies, the information signal is the sum of the observed data and the noise [7]. Noise in this paper is a gaussian noisse with impedance value is 40 ohm, 50 ohm and 60 ohm. At Figure 3 represent difference value of the noise amplitude .
Proceeding of The 1st International Joint Conference Indonesia-Malaysia-Bangladesh-Ireland 2015 Universitas Ubudiyah Indonesia, 27-28 April 2015, Banda Aceh, Indonesia
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Figure 3 histogram and power spectral density of gaussian noise in difference impedance Figure 4 shows a scatter plot of the voice signal and noise signal, on the graph shows that the noise and signal noise there is no correlation or independent. Therefore, ICA technique can be used as a reducing noise as one of the principal use ICA algorithm is data independent [8]. In figure 4 it can be seen in addition to the correlation between the noise and the sound signal is also seen histograms of the two pieces of the signal. Noise signal (x axis) has a Gaussian distribution, while the voice signal (y-axis) has nongaussian distribution. Based on the principle of ICA one nongaussian distribution of data must have to do with the separation process ICA algorithm [4].
Figure 4 scattering plot between noise signal and information signal
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Noise that occurs can destroy the information signal, as shown in Figure 5 is the spectrograms of the signal without noise (above) with a signal to noise (below). Spectogram type used is a type of wideband spectogram. Wideband spectogram a spectral analysis on a 15-millisecond intervals using a filter with a bandwidth of 125 Hz as well as detailed analysis of every 1 millisecond. In spectogram be seen that the signal to noise has spectogram with the original signal, noise often found in high frequencies.
Information on the human voice is contained in the low frequency 0-5 kHz as shown in figure 3. At higher frequencies in the sound information will decay and disappear, while the sound signal is given gaussian noise damaging information so that the information remains at low frequencies but at higher frequencies there is information that is damaging the information.
Therefore we need a noise reduction system, many algorithms have been made to reduce noise such as LPF and BPF [9], wavelet [10], and ICA [8].
Figure 5 spectogram of information signal with noise (bottom) and without noise (up) After the noise reduction process using ICA calculation of the separation quality by comparing the value of jitter, shimmer, and HNR of the signal before separation and after
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separation. Jitter error value on the third different impedance value has a value approximately equal to the maximum value of the jitter error of 0.015957. In shimmer has the same characteristics with the jitter error due to jitter and shimmer processing in the frequency domain is a domain. Maximum error value on the shimmer of 0.711758. HNR error values with different noise impedance values have diverse characteristics of error, it is because the calculation HNR based on the signal amplitude. The highest error on HNR value of 2.5376. The highest error value lies in noise with value -40 ohm impedance. At -40 ohm impedance value of the power spectral density of -90 dB / Hz and highest value between -50 and -60 ohm ohms.
Figure 6 error of acoustic parameter after ICA process
4 Conclusion
Simulation of noise reduction in this paper to determine the performance of ICA algorithm to reduce noise on the telephone network. Simulations carried out by summing the signal information with gaussian noise with a sound different impedance values. The greater the impedance niali given as noise, power spectral density values greater that mengabitkan error value the greater the quality of the acoustic parameters. The largest error value -40 ohm impedance spectral density value of -90 dB/Hz. Error value of acoustic parameters is still large that need to be postprocessing to improve the quality of noise reduction.
The simulation proved that fastICA can be used to reduce noise in a cellular network that will be used as a reducing noise for detection vocal cord disorder in the mobile network will be done next research.
References
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