Bioacoustic complexity index method with low sensitivity to noise influence

Abstract

The invention discloses a bioacoustic complexity index method with low sensitivity to noise influence, which comprises the following steps of: performing short-time discrete Fourier transform on field bioacoustic monitoring recording data in an analysis time period to obtain a time-frequency power spectrum of the field bioacoustic monitoring recording data; respectively estimating the narrow-band noise average power of each frequency point by using the noise time-frequency point set which does not contain the biological sound signal; respectively carrying out power spectrum subtraction processing on each frequency point to obtain a biological sound time-frequency power spectrum after noise reduction and enhancement; performing time-frequency domain subdivision on the analysis bandwidth and the analysis time period according to a certain frequency step length and time step length, and calculating a time-frequency partition index value; and accumulating all the time-frequency partition index values in the analysis bandwidth and the analysis time period to obtain a final biological acoustic index value, namely a noise reduction acoustic complexity index based on the biological sound time-frequency power change after noise reduction enhancement. According to the method, the sensitivity of an index result to noise influence is greatly reduced, and the space-time application range of similar acoustic index strategies is remarkably expanded.

Description

Technical Field

[0001] The present invention relates to the field of rapid assessment of biological species diversity and acoustic signal processing, and in particular to a bioacoustic complexity index method with low sensitivity to noise. Background Art

[0002] Many organisms in nature make sounds, which are rich in important biological information. Compared with traditional field survey methods, Passive Acoustic Monitoring (PAM) has the advantages of low overall cost, little disturbance to habitats, and the ability to provide long-term data records. It has great room for development in biodiversity conservation, natural resource management, and ecosystem monitoring. With the popularization of PAM, the demand for efficient analysis and processing of field monitoring data in order to quickly extract meaningful biological and ecological information from natural soundscapes has become increasingly strong. The acoustic index method that has emerged in recent years is just such a technical means to achieve rapid assessment of biodiversity by targeted measurement and quantification of the characteristics of biological sound signals in actual recordings. It has received more and more attention and application at home and abroad.

[0003] In the field of ecological monitoring, more than 60 bioacoustic indices have been developed. Among them, the Acoustic Complexity Index (ACI) (Pieretti N, Farina A, Morri DA new methodology to infer the singing activity of an avian community: The Acoustic Complexity Index (ACI) [J]. Ecological indicators, 2011, 11 (3): 868-873.) is one of the most popular, most studied and most widely used indices. This index is often considered to be able to capture the dynamic changes and complexity of biological communities and ecosystems, and is often used to quickly assess the species richness of organisms in habitats. However, in practical applications, whether there is a clear relationship between ACI and biological species richness, and its overall effectiveness in different ecosystems and different biological communities, has always been a matter of great controversy. This is because the field monitoring recording data actually used to calculate ACI is not a pure biological sound signal, but a mixed signal containing both biological sound and various noise components. Although ACI is designed with certain considerations for suppressing stationary noise and line spectrum interference, the exponential calculation process based on time-frequency intensity changes rather than power changes has relatively limited noise suppression capabilities in principle, resulting in ACI being still very sensitive to noise interference and its changes. Its value will deviate more and more from the ideal value corresponding to the pure bioacoustic signal under noise-free conditions as the bioacoustic signal-to-noise ratio (SNR) in the actual soundscape decreases. Since the actual bioacoustic signal intensity is affected by a variety of uncontrollable factors such as the sound source size, distance, and number of sound sources, the soundscape environment, weather conditions, and biological populations at different monitoring locations and time periods are also very different. Therefore, the bioacoustic SNR in actual field recording data fluctuates greatly with time and space, and the bioacoustic SNR in most data periods is relatively low, with very few times exceeding 20dB. It can be seen that the high sensitivity of ACI values ​​to changes in bioacoustic SNR makes it unable to undertake stable and reliable biodiversity monitoring tasks, making it difficult to achieve large-scale promotion and application in the field of ecological monitoring. Therefore, in order to enhance the universality of the ACI index design strategy in various ecological monitoring soundscape environments and make it a truly reliable and effective tool for rapid biodiversity assessment, it is urgent to improve its processing to increase its robustness to noise impact. Summary of the invention

[0004] The object of the present invention is to provide a bioacoustic complexity index method which is less sensitive to noise.

[0005] The technical solution to achieve the purpose of the present invention is: a method for bioacoustic complexity index with low sensitivity to noise, the steps are as follows:

[0006] Step 1: Perform short-time discrete Fourier transform on the field biological sound monitoring recording data in the analysis period to obtain its time-frequency power spectrum;

[0007] Step 2: using the time-frequency power spectrum of the recording data obtained in step 1 and a set of noise time-frequency points that do not contain biological sound signals, respectively estimate the average power of narrowband noise at each frequency point;

[0008] Step 3, using the time-frequency power spectrum of the recording data obtained in step 1 and the average power of the narrowband noise at each frequency point obtained in step 2, respectively perform power spectrum subtraction processing on each frequency point to obtain the time-frequency power spectrum of the biological sound after noise reduction and enhancement;

[0009] Step 4: Using the noise-reduced and enhanced biological sound time-frequency power spectrum obtained in step 3, subdivide the analysis bandwidth and analysis period in the time-frequency domain according to a certain frequency step and time step. Each time-frequency partition corresponds to a number of frequency points and a number of time frames. The sum of the absolute values ​​of the power differences of all adjacent time-frequency points in each time-frequency partition is used as the numerator, and the sum of the powers of all time-frequency points is used as the denominator. The time-frequency partition index value is obtained by calculating the ratio of the two.

[0010] Step 5: Using all the time-frequency partition index values ​​obtained in step 4, all the time-frequency partition index values ​​within the analysis bandwidth and analysis period are accumulated to obtain the final bioacoustic index value, that is, the noise reduction acoustic complexity index based on the change of bioacoustic time-frequency power after noise reduction enhancement.

[0011] Furthermore, the specific process of step 1 is as follows:

[0012] The recorded data of field biological sound monitoring in the analysis period are processed by frame division; each frame of data is windowed and processed by short-time discrete Fourier transform to obtain the time-frequency power spectrum P(k,q), 1≤k≤K,1≤q≤Q, where k and q are the frame number and frequency point number respectively, K is the total number of frames in the analysis period, and Q is the total number of frequency points in the analysis bandwidth.

[0013] Furthermore, the specific process of step 2 is as follows:

[0014] Step 2-1, obtain a background noise time-frequency point set according to the time-frequency power spectrum of the recording data, the frame number set of pure noise time-frequency points that do not contain biological sounds in the qth frequency point is recorded as Ω(q), and the number of noise time-frequency points in the set Ω(q) is recorded as N(q);

[0015] Step 2-2: Calculate the average power of the narrowband noise at each frequency point according to the noise time-frequency point frame number set Ω(q) and the number of noise time-frequency points N(q) obtained in step 2-1:

[0016]

[0017] Furthermore, the specific process of step 3 is as follows:

[0018] Using the time-frequency power spectrum of the bio-sound monitoring recording data obtained in step 1 and the average power of the narrowband noise at each frequency point obtained in step 2, a relaxation factor γ is set that is not greater than 1, and then the spectral subtraction method is used to calculate the bio-sound time-frequency power spectrum P′(k,q) after noise reduction and enhancement, and zero is used as the lower limit of the time-frequency power after noise reduction:

[0019]

[0020] Furthermore, the specific process of step 4 is as follows:

[0021] Step 4-1: Subdivide the analysis bandwidth and analysis period in the time-frequency domain according to the set frequency step and time step. Each frequency step includes i frequency points. The frequency point set within the nth frequency step is:

[0022] Ω n ={q|(n-1)i<q≤ni}

[0023] Each time step includes j time frames, so the set of time frames in the mth time step is:

[0024] Ψ m ={k|(m-1)j<k≤mj}

[0025] Step 4-2: Calculate the absolute value of the power difference D(m,n) of all adjacent time-frequency points in each time-frequency partition and the power sum A(m,n) of all time-frequency points:

[0026]

[0027] Step 4-3: Calculate the ratio of the sum of the absolute values ​​of the power differences D(m,n) in each time-frequency partition to the total power A(m,n) to obtain the time-frequency partition index value. If the total power is 0, the time-frequency area is considered to be a full noise area with no biological acoustic activity, and the index value is directly set to 0:

[0028]

[0029] Furthermore, the specific process of step 5 is as follows:

[0030] The final bioacoustic index value DACI is obtained by summing up all the time-frequency partition index values ​​within the analysis bandwidth and analysis period:

[0031]

[0032] Where n start 、n end are the starting frequency step number and the ending frequency step number of the analysis bandwidth, respectively. start 、m end They are the starting time step number and the ending time step number of the analysis period respectively.

[0033] An electronic device comprises a microphone, a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned bioacoustic complexity index method with low sensitivity to noise is implemented.

[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned bioacoustic complexity index method with low sensitivity to noise.

[0035] A computer program product comprises a computer program, which implements the above-mentioned bioacoustic complexity index method with low sensitivity to noise when executed by a processor.

[0036] Compared with the prior art, the beneficial effects of the present invention are: 1) the design defects of conventional ACI are corrected in principle, and a new strategy of bioacoustic complexity index with low sensitivity to noise is proposed, namely, the noise reduction acoustic complexity index DACI based on the change of bioacoustic time-frequency power after noise reduction enhancement; 2) without affecting the existing ecological interpretation and research accumulation of conventional ACI, the robustness of the actual index value to the large-scale random changes of bioacoustic SNR over time is significantly improved, which is helpful to achieve large-scale promotion and application in the field of ecological monitoring; 3) the calculation is simple and easy to implement, providing a reliable and robust solution for efficient analysis and processing of large-scale field monitoring recording data.

[0037] The present invention is further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The figure is a flow chart of the method of the present invention.

[0039] Figure 2 The time domain waveform diagram and corresponding time-frequency spectrum diagram of the synthetic noisy bird sound signal data are obtained by superimposing the pure bird sound signal data with the same time-frequency distribution structure according to the two preset SNRs of 40dB and -20dB on the same noise background.

[0040] Figure 3 The graph shows the change of the index value calculated by ACI and DACI for the synthetic noisy bird sound signal data with the same time-frequency distribution of the bird sound signal as the bird sound SNR changes.

[0041] Figure 4 When the bird sound SNR is set to 40dB and -20dB respectively, the time-frequency partition index values ​​calculated by ACI and DACI for the synthetic noisy bird sound signal data with the same time-frequency distribution of the bird sound signal change along the frequency axis.

[0042] Figure 5 This is a comparison chart of the ACI and DACI numerical distributions calculated using 200 sets of randomly selected actual field monitoring recording data with a higher SNR of bird sounds above 25dB. DETAILED DESCRIPTION

[0043] The present invention is oriented to the needs of ecological acoustic monitoring and quality assessment, and proposes a bioacoustic complexity index that is insensitive to noise, namely, the Denoising Acoustic Complexity Index (DACI) based on the change in bioacoustic time-frequency power after noise reduction and enhancement. DACI utilizes the statistical independence between bioacoustic signals and noise signals and the statistical characteristics that the noisy bioacoustic power is equal to the sum of the bioacoustic power and the noise power. By changing the intensity subtraction operation of the ACI's temporally adjacent time-frequency points to the power subtraction operation after spectral subtraction and noise reduction, it can effectively suppress the influence of background noise on the index results while retaining the bioacoustic time-frequency structure information and its intrinsic power changes as much as possible, thereby obtaining the acoustic statistical characteristics of the biological community under ideal conditions close to noise-free. The DACI proposed in the present invention can still have a high numerical robustness when the bioacoustic SNR is as low as -20dB, and has a strong correlation with the result values ​​of conventional ACI under high SNR conditions. Therefore, without affecting the existing ecological interpretation and research accumulation of conventional ACI, the present invention can significantly improve the temporal and spatial universality of similar bioacoustic index design routes in various ecological monitoring soundscape environments, providing an efficient and feasible new idea and a reliable and robust new tool for large-scale promotion of rapid biodiversity assessment applications in the field of ecological monitoring.

[0044] Combination Figure 1 The present invention is a bioacoustic complexity index method with low sensitivity to noise. In view of the fact that birds are very sensitive ecological monitoring indicator organisms, the following uses birds as research objects and illustrates the specific implementation of the present invention through a specific example of rapid assessment of bird song diversity in the wild. The steps are as follows:

[0045] Step 1: Perform short-time discrete Fourier transform on the field bird sound monitoring recording data during the analysis period to obtain its time-frequency power spectrum:

[0046] The wild bird sound monitoring recording data in the analysis period is framed, and each frame of data is windowed and then short-time discrete Fourier transform processed to obtain a time-frequency power spectrum P(k,q), 1≤k≤K, 1≤q≤Q, wherein k and q are the frame number and the frequency point number respectively, K is the total number of frames in the analysis period, and Q is the total number of frequency points in the analysis bandwidth. In a specific example of the present invention, the data sampling rate is 32kHz, the analysis period length is 60s, the frame length and frame shift are both 16ms, the corresponding K and Q are 3750 and 256 respectively, the number of samples of the short-time discrete Fourier transform is 512, and a Hamming window is added before the short-time discrete Fourier transform.

[0047] Step 2: Using the time-frequency power spectrum of the recording data obtained in step 1, and the set of noise time-frequency points that do not contain bird sound signals, estimate the average power of narrowband noise at each frequency point:

[0048] Step 2-1, use the energy probability histogram to set a double threshold to extract background noise, and obtain a set of noise time-frequency points that do not contain bird sound signals. The specific implementation method is: perform probability statistics on the power of all frames corresponding to each frequency point, find the power T corresponding to the maximum probability value from the probability histogram, set the upper threshold T1=T+0.1 and the lower threshold T2=T+0.001, mark the time-frequency points with power lower than the lower threshold T2 as noise time-frequency points, and form sets with the frame numbers of the noise time-frequency points marked by each frequency point. The set of noise time-frequency point frame numbers corresponding to the qth frequency point is recorded as Ω(q), and the number of noise time-frequency points in the set Ω(q) is recorded as N(q);

[0049] Step 2-2: Calculate the average power of the narrowband noise at each frequency point according to the noise time-frequency point frame number set Ω(q) and the number of noise time-frequency points N(q) obtained in step 2-1:

[0050]

[0051] Step 3: Using the time-frequency power spectrum of the recording data obtained in step 1 and the average power of the narrowband noise at each frequency point obtained in step 2, perform power spectrum subtraction on each frequency point to obtain the time-frequency power spectrum of the bird sound after noise reduction and enhancement:

[0052] Using the time-frequency power spectrum of the bird sound monitoring data obtained in step 1 and the average power of the narrowband noise obtained in step 2, the relaxation factor γ is set to 0.618 in the specific example of the present invention. Then, the spectral subtraction method is used to calculate the time-frequency power spectrum of the bird sound after noise reduction and enhancement, and zero is used as the lower limit of the time-frequency power after noise reduction:

[0053]

[0054] Step 4: Use the noise-reduced and enhanced bird sound time-frequency power spectrum obtained in step 3 to subdivide the analysis bandwidth and analysis period in the time-frequency domain according to a certain frequency step and time step. Each time-frequency partition corresponds to a number of frequency points and a number of time frames. The sum of the absolute values ​​of the power differences of all adjacent time-frequency points in each time-frequency partition is used as the numerator, and the sum of the powers of all time-frequency points is used as the denominator. The time-frequency partition index value is obtained by calculating the ratio of the two:

[0055] Step 4-1, subdividing the analysis bandwidth and analysis period in the time-frequency domain according to a certain frequency step and time step, each frequency step includes i frequency points, in the specific example of the present invention, i is 1, the corresponding frequency step is 62.5Hz, then the frequency point set in each frequency step contains only one frequency point element, and the frequency point set in the nth frequency step is:

[0056] Ω n ={q|(n-1)i<q≤ni}={q|q=n}

[0057] Each time step includes j time frames. In the specific example of the present invention, j is 313, corresponding to a time step of about 5 seconds. Then, each time step contains 313 time frame elements. The time frame set in the mth time step is:

[0058] Ψ m ={k|(m-1)j<k≤mj}

[0059] Step 4-2: Calculate the absolute value of the power difference D(m,n) of all adjacent time-frequency points in each time-frequency partition and the power sum A(m,n) of all time-frequency points:

[0060]

[0061] Step 4-3: Calculate the ratio of the sum of the absolute values ​​of the power differences D(m,n) in each time-frequency partition to the total power A(m,n) to obtain the time-frequency partition index value. If the total power is 0, the time-frequency area is considered to be a completely noisy area with no bird sound activity, and the index value is directly set to 0:

[0062]

[0063] Step 5: Using all the time-frequency partition index values ​​obtained in step 4, all the time-frequency partition index values ​​within the analysis bandwidth and analysis period are accumulated to obtain the final bioacoustic index value, that is, the noise reduction acoustic complexity index DACI based on the change of bioacoustic time-frequency power after noise reduction enhancement:

[0064]

[0065] Where n start 、nend are the starting frequency step number and the ending frequency step number of the analysis bandwidth, respectively. start 、m end are the starting time step number and the ending time step number of the analysis period, respectively. In the specific example of the present invention, the analysis bandwidth is 500Hz-12kHz, and the corresponding n start Take 9, n end Take 192, the analysis period length is 60s, and the corresponding m start Take 1, m end Take 12.

[0066] In one embodiment, an electronic device is provided, including a microphone, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned bioacoustic complexity index method with low sensitivity to noise when executing the program.

[0067] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method for calculating the bioacoustic complexity index with low sensitivity to noise is implemented.

[0068] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the above-mentioned bioacoustic complexity index method with low sensitivity to noise effects.

[0069] Figure 2 In the figure, (a) and (b) are the time domain waveform and corresponding time-frequency spectrum of the synthetic noisy bird sound signal data obtained by superimposing the pure bird sound signal data with the same time-frequency distribution structure on the same noise background according to two preset SNRs of 40dB and -20dB. Under the condition of 40dB high SNR, the bird sound signal is clearly visible, while under the condition of -20dB low SNR, the bird sound signal is almost covered by the background noise.

[0070] Figure 3 The curves of the exponential values ​​calculated by ACI and DACI for synthetic noisy bird sound signal data with the same time-frequency distribution of bird sound signals as the bird sound SNR are given. It can be seen that when the bird sound signals have the same time-frequency distribution, ACI shows an obvious monotonically decreasing change as the SNR decreases, while the calculation results of DACI do not change significantly even when the SNR is as low as -20dB, which shows that ACI is more sensitive to noise, while DACI shows significantly better noise robustness than ACI.

[0071] Figure 4The graphs of the time-frequency partition index values ​​calculated by ACI and DACI for synthetic noisy bird sound signal data with the same time-frequency distribution of bird sound signals along the frequency axis are given when the bird sound SNR is set to 40dB and -20dB respectively. It can be seen that under low SNR conditions, the ACI time-frequency partition index values ​​of all frequency bands are lower than those under high SNR conditions, while the various time-frequency partition indexes of DACI maintain a high degree of consistency in both numerical values ​​and change trends along the frequency axis under high and low SNR conditions, and the two curves fit very well. This figure shows that DACI not only shows a high degree of robustness in the overall numerical results of the full frequency band, but also shows a high degree of consistency in the results for each individual frequency component.

[0072] Figure 5 A comparison chart of the numerical distribution of ACI and DACI calculated using 200 sets of randomly selected actual field monitoring recording data with a high bird sound SNR of more than 25dB is given. It can be seen that under high SNR conditions, the numerical change trends of DACI and ACI are basically the same, and the Spearman correlation coefficient between the two is calculated to be 0.83, showing a strong correlation. This result shows that although DACI focuses on time-frequency power changes rather than intensity changes, its technical route is rooted in the theoretical assumptions and design concepts of ACI. Therefore, the introduction of DACI does not affect the existing ecological interpretation and research accumulation of ACI.

Claims

1. A bioacoustic complexity index method with low sensitivity to noise, It is characterized in that The steps include: Step 1: Perform short-time discrete Fourier transform on the field biological sound monitoring recording data in the analysis period to obtain its time-frequency power spectrum; Step 2: using the time-frequency power spectrum of the recording data obtained in step 1 and a set of noise time-frequency points that do not contain biological sound signals, respectively estimate the average power of narrowband noise at each frequency point; Step 3, using the time-frequency power spectrum of the recording data obtained in step 1 and the average power of the narrowband noise at each frequency point obtained in step 2, respectively perform power spectrum subtraction processing on each frequency point to obtain the time-frequency power spectrum of the biological sound after noise reduction and enhancement; Step 4: Using the noise-reduced and enhanced biological sound time-frequency power spectrum obtained in step 3, subdivide the analysis bandwidth and analysis period in the time-frequency domain according to the set frequency step and time step. Each time-frequency partition corresponds to a number of frequency points and a number of time frames. The sum of the absolute values ​​of the power differences of all adjacent time-frequency points in each time-frequency partition is used as the numerator, and the sum of the powers of all time-frequency points is used as the denominator. The time-frequency partition index value is obtained by calculating the ratio of the two. Step 5: Using all the time-frequency partition index values ​​obtained in step 4, all the time-frequency partition index values ​​within the analysis bandwidth and analysis period are accumulated to obtain the final bioacoustic index value, that is, the noise reduction acoustic complexity index based on the change of bioacoustic time-frequency power after noise reduction enhancement.

2. A method for a bioacoustic complexity index with low sensitivity to noise according to claim 1, Features: The specific process of step 1 is as follows: The recorded data of field biological sound monitoring in the analysis period are processed by frame division; each frame of data is windowed and processed by short-time discrete Fourier transform to obtain the time-frequency power spectrum P(k,q), 1≤k≤K,1≤q≤Q, where k and q are the frame number and frequency point number respectively, K is the total number of frames in the analysis period, and Q is the total number of frequency points in the analysis bandwidth.

3. A method for a bioacoustic complexity index with low sensitivity to noise according to claim 2, Features: The specific process of step 2 is as follows: Step 2-1, obtain a background noise time-frequency point set according to the time-frequency power spectrum of the recording data, the frame number set of pure noise time-frequency points that do not contain biological sounds in the qth frequency point is recorded as Ω(q), and the number of noise time-frequency points in the set Ω(q) is recorded as N(q); Step 2-2: Calculate the average power of the narrowband noise at each frequency point according to the noise time-frequency point frame number set Ω(q) and the number of noise time-frequency points N(q) obtained in step 2-1:

4. A method for a bioacoustic complexity index with low sensitivity to noise according to claim 3, Features: The specific process of step 3 is as follows: Using the time-frequency power spectrum of the bio-sound monitoring recording data obtained in step 1 and the average power of the narrowband noise at each frequency point obtained in step 2, a relaxation factor γ is set that is not greater than 1, and then the spectral subtraction method is used to calculate the bio-sound time-frequency power spectrum P′(k,q) after noise reduction and enhancement, and zero is used as the lower limit of the time-frequency power after noise reduction:

5. A method for a bioacoustic complexity index with low sensitivity to noise according to claim 4, It is characterized by: The specific process of step 4 is as follows: Step 4-1: Subdivide the analysis bandwidth and analysis period in the time-frequency domain according to the set frequency step and time step. Each frequency step includes i frequency points. The frequency point set within the nth frequency step is: Ω n ={q|(n-1)i<q≤ni} Each time step includes j time frames, so the set of time frames in the mth time step is: P m ={k|(m-1)j<k≤mj} Step 4-2: Calculate the absolute value of the power difference D(m,n) of all adjacent time-frequency points in each time-frequency partition and the power sum A(m,n) of all time-frequency points: Step 4-3: Calculate the ratio of the sum of the absolute values ​​of the power differences D(m,n) in each time-frequency partition to the total power A(m,n) to obtain the time-frequency partition index value. If the total power is 0, the time-frequency area is considered to be a full noise area with no biological acoustic activity, and the index value is directly set to 0:

6. A method for a bioacoustic complexity index with low sensitivity to noise according to claim 5, It is characterized by: The specific process of step 5 is as follows: The final bioacoustic index value DACI is obtained by summing up all the time-frequency partition index values ​​within the analysis bandwidth and analysis period: Where n start 、n end are the starting frequency step number and the ending frequency step number of the analysis bandwidth, respectively. start 、m end They are the starting time step number and the ending time step number of the analysis period respectively.

7. An electronic device comprising a microphone, a memory, a processor, and a computer program that can be run on the processor, It is characterized in that When the processor executes the program, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the program is executed in a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer program product comprising a computer program, It is characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 6 are implemented.

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