U he diva oversampling download#

If you want true analogue emulation there is 'U-HE DIVA' considered to be the most 'analogish' analogue emulation. In the next tutorial I will show how we can modify this structure to create a filter for use in oversampling.All in all, it is the most amazing analog emulation I have ever heard. The results are stored in an Event table where they can be saved as a text file and loaded in a Core table that can control the Data input of the Slow FIR macro, or better yet, a faster macro based upon the more efficient tap structure shown above. The ensemble calculates and stores the coefficients of a filter specified by the user.

U he diva oversampling download#

You can download a sample ensemble that demonstrates the use of the macros introduced in this tutorial here. This is due to the fact that this macro contains a substantial amount of overhead that really is only necessary for testing purposes (giving us a variable number of taps to choose an optimal number for a final design).ДЄ faster version might look something like this:ДЄlthough in this case we lose the ability to change the number of taps at will. Note that this implementation is far from the most efficient realization of a windowed sinc filter. Finally, the input is delayed by a sample and sent to the output.Д«y linking several of these macros together in a chain, you are creating an FIR filter, where the Tot output of the final tap is the output of the filter. The -> holds the filter input, which gets multiplied by the coefficient and added to the running total. The Pos value controls which coefficient is used for the tap, while the Tot value holds a running total of the sum of all previous taps. The Pos, Tot, and -> values are passed from the output of one copy into the inputs of another like so: Each copy represents one tap of the filter. This macro has been designed to chain together multiple copies. With this in mind, I created a simple macro to use for designing new filters in real time: In this instance, the coefficients will be constant and can be stored in a table.

Instead, it has uses as an oversampling filter. While a windowed sinc filter has some desirable properties, itвЂ™s lack of resonance and expensive coefficient calculation make it a bad fit for use as a standard audio shaping filter in a synth. The I/M module gives us the data point between 0 and 1 to sample from.ДЄfter using the incoming value I to sample a point from each function, we can multiply the values together and store the product as a coefficient to be used in the filter. Therefore, we must collect M data points between 0 and 1 from the window function. The relevant area of the window ranges from 0 to 1, while the sinc function has M points.

The Blackman window must be computed also, and stretched to fit over the sinc function. Thus, we check to make sure the I and M/2 before moving forward.

Second, if I вЂ“ M/2 is equal to zero, the function will give an error (division by zero). We are sampling the sinc function then, from a range of -M/2 to M/2. This value is then used as the I value in the sinc function listed above (Eq 1). First, note that we end up subtracting M/2 from our incoming I value, which ranges from 0 to M. It should range from 0 to 0.5, hence we can simply divide the cutoff frequency by the sampling rate.

First, we need to normalize the FC value, as is typical in filters. The Order modules make sure that the current values for FC and M are received before the iteration begins. The cutoff value (FC), number of taps -1 (M), and iteration index (I) are sent to the Windowed Sinc cell to do some work. Therefore, I used an iteration module that is triggered upon receiving a new cutoff value or number of coefficients: The number of taps defines how many data points we will take from both the sinc function and blackman window function, and is directly related to how much CPU the filter will use. To implement this filter, we need two pieces of information from the user вЂ“ the cutoff frequency, and the number of taps, or coefficients, to use. Now, we have a finite function defining our impulse response.