Automatic monitoring of group-housed pigs in real time through porcine acoustic signals has played a crucial role in automated farming. In the process of data collection and transmission, acoustic signals are generally interfered with noise. In this paper, an effective porcine acoustic signal denoising technique based on ensemble empirical mode decomposition (EEMD), independent component analysis (ICA), and wavelet threshold denoising (WTD) is proposed. Firstly, the porcine acoustic signal is decomposed into intrinsic mode functions (IMFs) by EEMD. In addition, permutation entropy (PE) is adopted to distinguish noise-dominant IMFs from the IMFs. Secondly, ICA is employed to extract the independent components (ICs) of the noise-dominant IMFs. The correlation coefficients of ICs and the first IMF are calculated to recognize noise ICs. The noise ICs will be removed. Then, WTD is applied to the other ICs. Finally, the porcine acoustic signal is reconstructed by the processed components. Experimental results show that the proposed method can effectively improve the denoising performance of porcine acoustic signal.
With the development of precision livestock farming, it is hard for breeders to monitor porcine abnormal states. Sound recognition, as one of the noncontact detection methods, has been proven to be a valuable method to detect some diseases [
Empirical mode decomposition (EMD) is an effective automatic decomposition algorithm to analyze nonlinear, nonstationary, and non-Gaussian signals [
In order to effectively eliminate the noise produced in the process of sound collection and transmission, EEMD-ICA-WTD, which can be employed before porcine sound recognition, is proposed in this paper. EEMD is used to decompose the porcine acoustic signal into IMFs. Then noise-dominant IMFs are distinguished by permutation entropy (PE). The independent components (IC) of noise-dominant IMFs are extracted by independent component analysis (ICA). As the first IMF contains much of the high-frequency noise [
This paper is organized as follows: Section 1 introduces the background significance of porcine acoustic signal denoising and the methods commonly used to eliminate the noises of different signals in recent years. Section 2 describes the porcine acoustic signals and the individual methods, including EEMD, PE, Fast-ICA, and WTD. The process of the proposed EEMD-ICA-WTD is presented in Section 3. The denoising performance evaluation indices, the simulation process, results of EEMD-ICA-WTD, and comparisons with other methods are presented in Section 4. Conclusions are drawn in Section 5.
The materials of this study are porcine acoustic signals. Below are the details of porcine acoustic signals. The methods are mainly comprised of EEMD, PE, Fast-ICA, and WTD, with a detailed explanation given below.
In this study, the original data are collected by an acoustic pickup device (ELITE model OS-100) from a large-scale pig farm in Shanxi Province, China. The schematic of the installation of the acoustic pickup in the pig farm is shown in Figure 1. The replacement gilts (PIC) at 5∼10 months old with weight ranging from 110 kg to 130 kg were studied in these experiments. Five replacement gilts were housed in a pigpen which is four meters wide and six meters long. The collection of sounds is controlled by the program developed in the numerical computing software (Python, ver. Python 3.5). The sampling frequency of the collected acoustic data is 1 kHz. The porcine acoustic signals we selected are scream [
PHOTO (COLOR): The installation drawing of the acoustic pickup in piggery.
The process of EEMD-ICA-WTD is mainly comprised of porcine acoustic signal decomposition based on EEMD, noise-dominant IMFs differentiation based on PE, independent components extraction by Fast-ICA, and denoising by WTD.
In this paper, the porcine acoustic signal is firstly decomposed. EMD can decompose the signals into IMFs from high to low frequency self-adaptively [
The local extreme points which are detected from the original signal s(t) can be connected by cubic curve spline and formed the upper and lower envelops. And the list m
The list h
The residual list r
The residual list r
Therefore, the original signal s(t) is decomposed as the sum of the IMF components and residual list r
Classical EMD may cause frequency aliasing during signal decomposition. In order to overcome this shortcoming, EEMD was proposed by Wu and Huang [
Add a random Gaussian white noise signal n^it to original signal s(t), which is defined as
where s
Decompose s
where c
Repeat the process as described above N times and add the different Gaussian white noises. Therefore, the original signal adds the Gaussian white noise N times. The means of IMFs can be obtained as
where c
Permutation entropy (PE), proposed by Bandt and Pompe [
For a given time series X = {x(i), i = 1, 2, ..., n}, the matrix of phase space reconstruction A is obtained as [
Each row of A can be arranged in ascending order [
Since the embedding dimension is m, there will be m! possible permutations. Each row of A can be represented by one of the permutations. P
The PE of order can be normalized as [
The ICA, as one of the multivariate statistical methods, is widely used in statistical sources separation [
The independent sources can be denoted as s(t) = [s
This function can be expressed by the matrix:
The aim of ICA is to estimate the inverse of the mixing matrix W, which could be used to calculate the independent signals.(
Fast-ICA algorithm is one of the improved ICA algorithms, which is widely utilized to estimate the orthogonal matrix. Fast-ICA has higher convergence speed compared to the conventional method and the step-size parameters are not needed. In this paper, Fast-ICA is used to extract independent components from IMFs.
Wavelet transform denoising (WTD) is one of the denoising algorithms based on wavelet transform (WT). WT can decompose signals at different scales. The discrete wavelet transform (DWT) is calculated as follows:(
The primary steps of WTD are described as follows:
Decompose the original signal by WT with proper wavelet basis function and decomposition level.
The threshold is performed by the selected proper threshold method for high-frequency coefficients at each decomposition scale. The low-frequency wavelet coefficient is kept unchanged.
The signal is reconstructed by the low-frequency coefficients and high-frequency coefficients after threshold processing.
It is crucial to select an appropriate threshold method for WTD. The common threshold selection methods fall into soft threshold method and hard threshold method [
The process of the new efficient denoising technique proposed in this paper is shown in Figure 2. It consists of five main steps explained as follows:
The porcine acoustic signal is decomposed into IMFs by EEMD. Sorted in the increasing order of IMFs, the frequency distribution of the IMFs varies from high to low. The noise mainly concentrates in high frequency. Therefore, the first few IMFs contain both the information of porcine acoustic signal and noise [
Denoising the noise-dominant IMFs directly may destroy the continuity of reconstructed signals. It is harmful to the denoising effect [
As the first IMF contains much of the high-frequency noise [
Denoise the other ICs by WTD. The wavelet basis function and decomposition level we selected are db6 and 3.
The denoised ICs (DICs) are transformed to denoised IMFs (DIMFs) through the matrix of mixing coefficients. Then the porcine acoustic signal is reconstructed by these DIMFs and real IMFs.
PHOTO (COLOR): The process chart of the proposed denoising technique for porcine acoustic signal.
This section introduces the simulation process and results of EEMD-ICA-WTD. In order to verify the performance of EEMD-ICA-WTD, the performance of the denoising is compared with the other six methods.
In order to analysis the process and results of EEMD-ICA-WTD, porcine scream and porcine cough are selected for denoising, taking the noisy scream with 5 dB SNRin by adding Gaussian white noise as an example. The time-domain waveforms of porcine scream and noisy scream are shown in Figure 3. The sampling frequency of the collected acoustic data is 1 kHz. Therefore, the porcine scream of 1 s contains 1000 sampling points.
PHOTO (COLOR): The time-domain waveforms of (a) porcine scream and (b) noisy scream.
The noisy scream is decomposed as step 1. The time-domain waveforms of IMFs and Res are shown in Figure 4. The noisy scream is divided into 9 IMFs of different frequencies and 1 residual. Each IMF contains different local characteristics of the original noisy scream.
PHOTO (COLOR): The decomposition result of the noisy scream.
The PE of each IMF is calculated as step 1. The time delay λ is commonly used as 1 and the embedding dimension m is commonly used as 3 [
PEs of noisy scream IMFs.
IMF1 IMF2 IMF3 IMF4 IMF5 IMF6 IMF7 IMF8 IMF9 Res 0.974 0.997 0.824 0.674 0.567 0.493 0.440 0.415 0.396 0
Table 1 shows that the PEs of IMF1, IMF2, IMF3, IMF4, and IMF5 are greater than 0.5. Therefore, these are noise-dominant IMFs.
The ICs of noise-dominant IMFs are extracted by Fast-ICA as step 2. The time-domain waveforms of ICs are shown in Figure 5. It can be observed that each IC concentrates more information.
PHOTO (COLOR): The time-domain waveforms of ICs.
The correlation coefficients are calculated as step 3. The correlation coefficients of ICs and the first IMF are shown in Table 2.
Correlation coefficients of each IC and the first IMF.
IC1 IC2 IC3 IC4 IC5 0.3222 0.4656 0.2772 0.8460 0.3170
Table 2 shows that the correlation coefficient between IC4 and the first IMF is larger than 0.8. Therefore, it should be removed.
The other ICs are denoised by WTD as step 4. The denoising results are shown in Figure 6.
PHOTO (COLOR): The denoising results of ICs by WTD.
The end result of the reconstructed signal is shown in Figure 7.
PHOTO (COLOR): The time-domain waveforms of the reconstructed porcine scream.
In order to evaluate the denoising performance of the method quantitatively, the root mean square error (RMSE), SNRout, and correlation coefficient (R) are adopted in this article [
RMSE reflects the degree of error between the denoised porcine acoustic signal and the original porcine acoustic signal. The smaller the value, the better the denoising effect. SNRout reflects the ratio of the porcine acoustic signal to real noise. Therefore, the higher the value, the less noise mixes. R is used to evaluate the correlation between the denoised porcine acoustic signal and the original porcine acoustic signal. The higher value represents the better denoising effect.
The performance of the denoising is shown in Table 3.
The denoising parameters of porcine scream.
RMSE SNRout 0.1476 6.2831 0.8992
In order to verify the performance of EEMD-ICA-WTD, six different denoising methods are used as comparison methods. They are EMD-TD [
Denoising results of porcine scream.
SNRin Parameter Denoising methods EMD-TD EMD-WTD EEMD-TD EEMD-WTD WSTD MBSS EEMD-ICA-WTD −10 RMSE 0.4794 0.3594 0.3952 0.3682 0.4319 0.7362 0.3389 SNRout/dB −3.9501 −1.4474 −2.2729 −1.6579 −3.0440 −8.6582 −1.0408 0.0633 0.3585 0.1723 0.3303 0.1762 0.0324 0.3701 −5 RMSE 0.4379 0.3597 0.3774 0.3643 0.4101 0.5214 0.3339 SNRout/dB −3.1633 −1.2094 −1.8728 −1.5668 −2.5937 −3.9452 −0.8091 0.1449 0.5380 0.2498 0.5343 0.3897 0.1167 0.5544 0 RMSE 0.4497 0.2533 0.3389 0.2601 0.2867 0.3241 0.2496 SNRout/dB −3.3946 1.5894 −0.9377 1.3619 0.5148 −0.8852 1.4573 0.1972 0.7590 0.4029 0.7779 0.7683 0.7091 0.7810 5 RMSE 0.4460 0.1474 0.3483 0.1491 0.1506 0.1543 0.1432 SNRout/dB −3.3242 6.2958 −1.1767 6.1937 6.1054 6.2438 6.4831 0.2381 0.9047 0.3955 0.9064 0.9029 0.8823 0.9145 10 RMSE 0.4315 0.0971 0.3321 0.0944 0.0935 0.0951 0.0913 SNRout/dB −3.0361 9.9147 −0.7613 10.1594 10.2463 10.0864 10.3106 0.2038 0.9480 0.4088 0.9518 0.9531 0.9487 0.9569
Denoising results of porcine cough.
SNRin Parameter Denoising methods EMD-TD EMD-WTD EEMD-TD EEMD-WTD WSTD MBSS EEMD-ICA-WTD −10 RMSE 0.4216 0.3100 0.3245 0.3163 0.3417 0.4775 0.3025 SNRout/dB −2.4745 0.1955 −0.1995 0.0217 −0.6479 −2.9245 0.2213 0.2337 0.4732 0.4832 0.4951 0.4389 0.2183 0.4978 −5 RMSE 0.3447 0.2629 0.2666 0.2773 0.2946 0.3341 0.2442 SNRout/dB −0.7254 1.6290 1.5076 1.1645 0.6390 −0.4762 1.9498 0.3903 0.6406 0.6656 0.6736 0.6075 0.4097 0.6752 0 RMSE 0.4168 0.2048 0.2464 0.2042 0.2423 0.2658 0.1880 SNRout/dB −2.3755 3.7967 0.1914 3.8236 2.3360 2.1935 4.0920 0.2767 0.7936 0.7359 0.8310 0.8257 0.7016 0.8353 5 RMSE 0.3941 0.1298 0.2762 0.1343 0.1362 0.1407 0.1224 SNRout/dB −1.8887 7.7609 1.1993 7.4601 7.3379 7.2837 7.9737 0.2712 0.9237 0.7403 0.9332 0.9293 0.8819 0.9360 10 RMSE 0.2777 0.0721 0.0952 0.0692 0.0842 0.0743 0.0646 SNRout/dB 2.1711 13.8798 11.4739 14.2417 12.5417 13.7961 14.6788 0.6897 0.9805 0.9689 0.9854 0.9828 0.9829 0.9859
The results, shown in Tables 4 and 5, are compared with different denoising methods evaluated using three parameters. According to the evaluations of RMSE, SNRout, and R, the EEMD-ICA-WTD has lower RMSE, higher SNRout, and R than the other six methods.
The results show that the EEMD-ICA-WTD proposed in this paper has the best denoising effects with different SNRins not only for porcine scream but also for porcine cough. The EEMD-WTD has the second-best denoising effects. Taking the denoising results for porcine cough as an example, when the SNRin of porcine cough is 10 dB, the values of RMSE, SNRout, and R after being denoised by EEMD-ICA-WTD are 0.0646, 14.6788, and 0.9859, respectively. These are close to the results of EEMD-WTD. The absolute differences of these three parameters between EEMD-ICA-WTD and EEMD-WTD are 0.0046, 0.4371, and 0.0005, respectively. With the increasing noise, the advantages of EEMD-ICA-WTD are more obvious. When the SNRin of porcine cough is −10 dB, the absolute differences of RMSE, SNRout, and R between EEMD-ICA-WTD and EEMD-WTD are 0.0138, 0.1996, and 0.0027, respectively. A large number of experiments for different kinds of porcine acoustic signals verify the universality of EEMD-ICA-WTD. In order to intuitively compare the performances of different denoising methods for porcine scream and porcine cough with different SNRins, the histograms are shown in Figures 8 and 9. Each histogram contains the denoising results of different methods with different SNRins. Different colors represent different SNRins. It can be observed that the RMSEs of EEMD-ICA-WTD with different SNRins are lower than the other six methods. And the SNRouts and Rs of EEMD-ICA-WTD are higher than the other six methods. In summary, the results show that the EEMD-ICA-WTD method is effective and suitable for porcine acoustic signal.
PHOTO (COLOR): Denoising results for porcine scream with different SNRins: (a) RMSE, (b) SNRout, and (c) R.
PHOTO (COLOR): Denoising results for porcine cough with different SNRins: (a) RMSE, (b) SNRout, and (c) R.
To improve the denoising performance of porcine acoustic signal, an efficient denoising technique based on EEMD-ICA-WTD is proposed in this paper. The approach has been developed with the purpose to reduce noise interference during the recognition of porcine abnormal sounds.
Firstly, the porcine acoustic signal is decomposed into different components in order of frequency. Because of the frequency aliasing of EMD, the EEMD is used to decompose the porcine acoustic signal into IMFs. As the noise mainly concentrates in high frequency, PE is used to distinguish the noise-dominant IMFs from the IMFs. Secondly, the continuity of the signal may be adversely affected if the noise-dominant IMFs are denoised directly. Therefore, the ICs of noise-dominant IMFs are extracted by Fast-ICA. The noise and real information are concentrated on the ICs. It has been shown that the first IMF contains much high-frequency noise. Therefore, the noise ICs are identified by correlation coefficients of ICs and the first IMF and are then removed. Finally, WTD is used for denoising the other ICs. The porcine acoustic signal is then reconstructed by processed ICs. The performance of this denoising method is shown to be superior to other methods.
In the future work, this approach will be optimized to reduce the run time on the premise of guaranteeing the performance of the denoising.
The porcine acoustic data used to support the findings of this study are included within the supplementary information file.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This study was supported by the National High Technology Research and Development Program of China (863 Program) (2013AA102306).
The supplementary materials are porcine acoustic signals of scream and cough. The data are saved as WAV format. The length of each signal is 1 s.
By Sunan Zhang; Jianyan Tian; Amit Banerjee and Jiangli Li