Serial noise reduction

I don't have the Noise Reduction stuff but just checked my account and all my software is listed along with SNs and download links. EDIT: actually the download links redirect to the Magix site. Now they are indeed listed in the Magix user area under My Products. Sorry for your trouble but thanks for posting this as I may not have ever looked to make sure those products had actually transferred.

I hope you get it squared away! The NR eventually became part of Soundforge. Sad that situations like this can cause some users illegitimate means of using their software. I just recently lost my C drive and had to recover all my apps including Sound Forge and Vegas.

However, I was lucky enough to have saved my download file from my old C drive which still had the installation files for those apps, and I was able to recover them. Perhaps you still have those installation files in your download file, too.

It sure saved my goose. I had to register all of my Sony's in Magix. Nothing transferred. BTW no upgrade pricing for this either. Might be time to find a new audio editor.

I somehow don't have the Serial numbers for the Noise Reduction as it was an add-on and it's a few years old now. The missing SN feature sends me in circles - this is total BS. But then Sony was never big on support. Serial still show in my Sony account. Something I always do with software is zip it and paste the serial number in the archive, along with the email saved as a text file. You are often at the mercy of web developers when it comes to stuff like this. This morning I got an email from them with a link to those files.

Perhaps if you send them an email, you will get the same result. If your name is in the Sony files, I suspect you'll be alright. Good luck. And then, nearly a year after posting this, suddenly, inexplicable, my plugins started working again. My original 5. LOL Michael. You can still go to Sonycreativesoftware. Of course it's now maintained by Magix but you're right the plugins bundles are missing.

Only the main programs. I've sent a request to support for clarification. Thanks Mike. I hadn't even thought about the downloads, since I'd got them during the transition and saved them.

Let us know when they respond. They were fairly quick on the first ticket and the second one may have been slow, due to my "operator error" ticket in the subject line.

After learning my lesson the hard way I started keeping all my serials on separate flash drive. Neat Video reduces that noise while preserving all details. Even very noisy shots can be saved. Underwater shooting is challenging. Camera has to work at its limit and that shows.

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Moreover, it's engineered to take full advantage of the available CPU and GPU hardware so it works as fast as possible. Technical product information tells one side of the tale. Better speed, better profiling and ease of use. The type I Chebyshev band-pass filter is designed to decrease the high frequency and low frequency environmental noises. The modified wavelet filter can reduce the environmental noises further and mitigate the second heart sounds and human voices.

The least-mean-square LMS adaptive filter with the modified reference signal is used to decrease the influence of the heart sounds. The principle of the serial integrated filter is introduced in this chapter.

Influenced by the environment factors, device factors and physical factors, the noise of lung sounds collected is concentrated in the low-frequency and high frequency.

To eliminate the main part of low-frequency and high-frequency noises, we choose a type I Chebyshev band-pass filter for ensuring a steep roll-off frequency. The frequency spectrum see Fig 3. For retaining the whole frequency domain of the lung sounds, the pass-band of FIR filter is set from 15Hz to Hz.

The output of the filter is shown in Fig 4. View in new window. But the DWT still has high calculation complexity. The signal f n to be decomposed is filtered by high-frequency filter h 0 n and low-frequency filter h 1 n.

Then the filtered signals are down-sampled to get the wavelet coefficients and approximation coefficients where j and k are respectively the dyadic dilation and dyadic position. In the next layer of wavelet decomposition, the approximation coefficients of former layer are transformed as the signal to be decomposed and processed in the same way.

The relationship of the two filters is. Then the signal is filtered by the high frequency filter and low frequency filter to get the wavelet coefficients and approximation coefficients. After the filters, the signal is down-sampled and a signal with half of the length is gotten.

After one step of decomposition, the approximation coefficients are processed in the same way to get wavelet coefficients and approximation coefficients in the next layer. After n steps of iteration, the signal is decomposed into n groups of wavelet coefficients and one group of approximation coefficients.

The wavelet reconstruction is the inverse operation of wavelet decomposition. Among the wavelet transform de-noising algorithms, the most widely used one is the wavelet threshold de-noising algorithm, which was firstly proposed by Donoho[ 17 , 18 ].

A contaminated signal with finite length is assumed to be expressed as:. Transform the noisy signal y i by discrete wavelet transform DWT. Let W be a left invertible wavelet transformation matrix of the DWT [ 19 ]. Then the Eq can be written as. The basic idea of wavelet threshold de-noising is to zero the wavelet coefficients belonging to the noise Z , while keep the wavelet coefficients belonging to the useful signal F , and the filtered signal approximates the desired signal f i.

The filtered signal x can be obtained by inverse DWT:. Several selection methods of threshold value play an important role in wavelet de-noising. Donoho proposed the thresholding rule [ 17 ]:. While Guoxiang[ 18 ] et al proposed the thresholding rule. However, sound signals are highly sensitive to the thresholding value selection. An excessive thresholding value can lead to the distortion of sound signals, while a small thresholding value may not achieve a good filtering result.

Therefore, the selection of thresholding values in this research cannot depend on a single fixed formula. In this research, a coif2 wavelet basis is chosen. The signal s is decomposed to 9 layers, as shown in Fig 6 , and nine sub-signals s i is reconstructed by each layer of wavelet coefficients.

The relationship of the original signal s and sub-signals s i is given by:. At the low resolutions layer 1 and 2 , there are only coefficients belonging to the noise at the high frequency domain. Therefore, the hard threshold method can be used to eliminate the high frequency noises directly. At most resolutions layer 3,4,5,6,9 , the proportion of useful signal components is higher, so the soft threshold method is chosen to avoid pseudo Gibbs phenomena. The selection of threshold value, as shown in Table 1 , depends on the repeated adjustment based on the low distortion and high de-noising effect rather than any fixed formula.

The majority components of the lung sound signal concentrate in the layer 7 and 8. A small deviation between the threshold value and ideal value may lead to a great distortion. Therefore, to ensure the low distortion of lung sound, the sub-signal is not processed by threshold de-noising filtered but the type I Chebyshev band-pass filter with the band-pass from 20Hz to Hz to eliminate the noises beyond the frequency band. The reconstructed signal is shown in Fig 8 , in which, the high-frequency and low-frequency noises, human voices and second heart sounds are largely reduced.

The threshold value is determined on the basis of the standard signal in this paper. However, the signal strength has a considerable difference with the standard signal for the different patient situations and measurement environments, which leads to the different selection of threshold value. The phenomenon limits the generalization of the algorithm among different signals collected. To solve the problem, a normalization method based on the power of signals is proposed. The power of a discrete signal is given by.

The original signal is normalized by. P 2 is the power of the signal to be processed. Because the arrangement can reduce the effect of environmental noises on the power of signal, the step of normalization is arranged after the step of Chebyshev band-pass filter, as shown in Fig 3.

Through this method, the strength of original signals is set to the similar magnitude of the standard signal, and the threshold value can be generalized on the signals with different signal strengths. There are three other signals acquired shown in Fig 9 , which have similarly favorable de-noising effect.

The first signal and the third signal belong to dry rales. The second signal belongs to the normal lung sound. Because the frequency of heart sounds overlaps with the low-frequency component of lung sounds, the filtering algorithms in frequency domain are not effective in this situation. The main approach in recent years to the problem is the adaptive filter.

In this paper, a least-mean-square LMS adaption algorithm referring to [ 7 , 16 , 21 ] is modified to reduce the first heart sound. The block diagram of the algorithm is shown in Fig The obtained signal d n is the output signal of the wavelet threshold filter containing the desired the pure lung sound signal s n and the first heart sound n n as inference.

The reference signal x n , which is linearly related with the inference signal n n , is obtained from the signal d n. The bias signal e n is obtained by the estimated signal n' n from the corrupted signal d n. In this way, the bias signal e n as the output of the filter can be more closed to the desired signal d n.

The cost function of LMS adaptive filter to get a desired estimation of the inference n n is the minimization of mean-square error [ 16 ], which is defined as:. Referring to [ 4 ], from Fig 10 , we can write.

Therefore , the inference n n approximates the estimated signal n' n , and the basis signal e n approximates the desired signal s n. That is the basic mechanism of the LMS adaptive filter for heart-noise reduction. The filtered results of the standard signal are shown in Fig Except some individual heart sounds, most heart sounds are found to be reduced by percent.