Spectral Plugin

[Krysta Cirilo]

Theproblem is that this plugin requires a host that is ARA compatible, which is not the case of WaveLab 9.5.

But in next WaveLab version, there will be a nice way to use SpectraLayers from WaveLab

I think you might be the only one who had an expectation that it should work in WL, seeing as WL is currently not an ARA2 host. The standalone SL6 application works, and the SL6 ARA2 plugin works in applications which are ARA2 hosts.

Gee all the Izotope RX plugins work great with WL AND I can also use it as a stand alone editor. SL6 does show up in WL so why would Steinberg do that IF they were not going to let WL users use it with WL???

I can understand how you feel, but as others pointed out, they did specify which programs it would work with as an ARA extension, and you can configure it to open the current file in another editor, be it Wavelab, or whatever. (See the SL manual for more info.)

One would have to make certain assumptions in order to conclude that it would work in Wavelab as it does in Cubendo. And of course, PG has responded to you specifically with an unambiguous and substantive answer to this.

Some of that brain-bending research from Paris' famed IRCAM is coming your way in plug-in form - even (partly) for free. It's a spectral remixing, sound morphing, audio shaping powerhouse that landed from some advanced alien civilization, but it's...

Hello

I tried to install Lumos on FIJI running in Mac but it makes FIJI crash.

I installed it by the update site but after that FIJI does not reopen.

I had to download FIJI again and reinstall all my plugin the only one that gave me problem was LUMOS.

Maybe , there is still something that is not compatible with Mac

essentially, spectralsand can break down the frequency spectrum into hundreds of tiny bits and delay each one by a certain amount of time. this allows it to completely change the timbre of whatever it's fed; but it gets better, because the amounts of time are organized in a graph that can be manipulated by a whole host of sources! you can use basic shapes, white noise, custom waveforms, and even a separate audio input! this unlocks strange new ways to make lasery noises, beautiful soundscapes, wet swampy sounds, and whatever else you can imagine.

osc type: choose from a variety of shapes to print onto the array. you can also change the frequency of the oscillators and invert their shapes. the full version of the plugin comes with adraw-your-own custom oscillator function as well.

sidechain mode (full version only): send spectralsand a sidechain input from another audio source (or maybe even from... itself?) to find unconventional combos of two sounds. (comes with a boost function to make the sidechain input more pronounced)

themes (full version only): spectralsand comes with the ability to load and make its' own themes in a matter of minutes. customize your copy of the plugin to fit your unique style! (also comes shipped with some of the best themes made by the spectralsand community)

this plugin was the end result of roughly 2 years of on-and-off work, all while dealing with school and learning to program in a brand new language called puredata. if you decide to buy the full version, i appreciate you so much and i hope you enjoy it. all i ask is that you share it with everyone you think will be interested in it.

MSpectralDynamics is essentially a dynamics processor which works in the spectral domain allowing you to work with individual frequencies. The compressor can make loud frequencies quieter for example - but that's just a beginning. With MSpectralDynamics you can expect nothing less than pristine audio quality!

Standard tasks, such as flattening the spectrum or removing noise, are available from the main screen with simple controls. Quick and easy, unless you want to dive deeper and make use of all the controls.

The plugin can do anything with each frequency. It can be a compressor, expander, gate, or any combination, and you can even draw your own processing shape for more creative effects. Add in integrated parallel compression, full channel linking, up to 8 channel surround processing and much more and you might just become a hopeless addict.

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In applications such as astronomy, medicine, physics and biology, scientists use digital images to record and analyze results from experiments. Environmental effects and imperfections in the imaging system can cause the recorded images to be degraded by blurring and noise. Image deconvolution (sometimes known as image deblurring) is the process of reconstructing or estimating the true image from the degraded one. Image deblurring algorithms can be classified into two classes: spectral filtering methods and iterative methods. Other classification divides the algorithms into methods that do not require any information about the blur (also called blind deconvolution) and the methods that need that information. The information about the blur is usually given in the form of a point spread function (PSF). A PSF is an image that describes the response of an imaging system to a point object. A theoretical PSF can be obtained based on the optical properties of the imaging system. The main advantage of this approach is that the obtained PSF is noise-free. The experimental technique, on the other hand, relies on taking a picture of a point object, for example a distant star. In this work we assume that the PSF is known and given in the form of an image.

The spectral filtering algorithms include many well known techniques for image deblurring such as Wiener filter and the pseudo inverse filter. But general approaches, such as truncated spectral decompositions and Tikhonov regularization also belong to this group. The key is to exploit the special structure of a PSF. For example, if fast Fourier transform (FFT) based methods are used (e.g., Wiener filter), then there is an implicit assumption that the blur is spatially invariant and that the original image scene is periodic (the so-called periodic boundary conditions). Other fast transforms, such as the discrete cosine transform (DCT) and the discrete sine transform (DST) can be used for other boundary conditions, but again these approaches only make sense if the blur is spatially invariant. Furthermore, in the case of using DCT and DST based methods, the PSF should be symmetric (as in the case of atmospheric turbulence). The advantage of using spectral filtering algorithms is that they can be very efficient, and they are fairly easy to implement. However, it is not possible to include additional constraints, such as nonnegativity, in this type of reconstruction methods.

There are seven drop-down lists (combo-boxes) available in the GUI. From the Image list, you can choose a blurred image. PSF list is for selection of a point spread function image. The content of these two lists depends on what is currently open in ImageJ - if no image windows are displayed then both lists are empty. The next two lists (Method and Stencil) allow you to choose an algorithm used for deconvolution (Generalized Tikhonov, Tikhonov or TSVD) and a stencil (for Generalized Tikhonov only). The stencil is used for creating a regularization matrix (an approximation of a derivative operator). A detailed explanation of all algorithms can be found in Deblurring Images: Matrices, Spectra, and Filtering. In the Resizing combo-box you can choose whether the blurred image will be padded to the next power-of-two size before processing. This feature is available mainly for performance reasons (FFT works faster when the size of the data is a power-of-two number). Note that if the size of each dimension of a blurred image is already a power-of-two number, then the image will not be padded even if the Next power of two option is selected. To display a padded image, the Show padded image check-box needs to be selected. The Output list is used to specify the type of an output (reconstructed image). Finally, in the Precision combo-box you can choose a floating-point precision used in computations. Practice shows that a single precision is sufficient for most problems.

There are few other important options in the GUI that require some explanation. The Threshold check-box and text field are used to remove a negative values from the solution (reconstructed image). Since it is not possible to impose nonnegativity constraints in the spectral algorithms, the threshold option is the only way to get a nonnegative solution. When the threshold option is enabled, then all values in the reconstructed image are that are less than the value specified in the threshold text field are replaced by zero. In the Max number of threads (power of 2) text field you can specify how many computational threads will be used. By default this value is equal to the number of CPUs available on your machine.

When deblurring an image, certain regularity conditions have to be enforced on the solution. The degree of regularization is determined by a regularization parameter that should be chosen carefully. Parallel Spectral Deconvolution has an option to automatically compute the value of a regularization parameter (Auto regularization parameter check-box). If this box is selected then the Generalized Cross-Validation algorithm is used (for details refer to Deblurring Images: Matrices, Spectra, and Filtering). Unfortunately, no parameter choice method is perfect, therefore it is possible to manually adjust the automatically computed value (Regularization parameter text field and slider). Once the initial deconvolution is finished, the Regularization parameter text field and slider, as well as the Update button are enabled. At this point, you can change the value of a regularization parameter either by using the slider or by entering a new value in the text field. The Update button is used to recompute the solution with the new value of a regularization parameter.

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