Waveform | lightweight framework for drawing waveform | iOS library

I'm answering my own question.

I realized that the only mistake I was doing in my Python code was not normalizing the autocorr values as was done in the Matlab example. I simply added the following in my code:

autocorr = (autocorr-min(autocorr))/(max(autocorr)-min(autocorr))

When I do so, I eventually get the desired results, same as that in the example:

Hence, to conclude, find_peaks() does in fact do the intended job.

Replace the async fragments something like this, assuming they are meant to be executed sequentially.

The error comes from the - in lazy val my-chip and package my-chip. If you want to use a - in a scala name you can wrap the name in backticks, like `my-chip`.

C passes arguments by value. That includes pointer arguments. Like all by-value arguments, changing the value within a function scope means nothing to the caller. If you want to convey a change in value to the caller there are multiple ways to do it, the most common shown below:

Use That Otherwise-Worthless Return Value

Right now your function returns void (e.g. nothing). Change it to send the (possibly updated) result of changes to arr. Like this:

In golang I have taken an array ARR1 which represents a time series ( could be audio or in my case an image ) where each element of this time domain array is a floating point value which represents the height of the raw audio curve as it wobbles ... I then fed this floating point array into a FFT call which returned a new array ARR2 by definition in the frequency domain where each element of this array is a single complex number where both the real and the imaginary parts are floating points ... when I then fed this array into an inverse FFT call ( IFFT ) it gave back a floating point array ARR3 in the time domain ... to a first approximation ARR3 matched ARR1 ... needless to say if I then took ARR3 and fed it into a FFT call its output ARR4 would match ARR2 ... essentially you have this time_domain_array --> FFT call -> frequency_domain_array --> InverseFFT call -> time_domain_array ... rinse N repeat

I know Web Audio API has a FFT call ... do not know whether it has an IFFT api call however if no IFFT ( inverse FFT ) you can write your own such function here is how ... iterate across ARR2 and for each element calculate the magnitude of this frequency ( each element of ARR2 represents one frequency and in the literature you will see ARR2 referred to as the frequency bins which simply means each element of the array holds one complex number and as you iterate across the array each successive element represents a distinct frequency starting from element 0 to store frequency 0 and each subsequent array element will represent a frequency defined by adding incr_freq to the frequency of the prior array element )

Each index of ARR2 represents a frequency where element 0 is the DC bias which is the zero offset bias of your input ARR1 curve if its centered about the zero crossing point this value is zero normally element 0 can be ignored ... the difference in frequency between each element of ARR2 is a constant frequency increment which can be calculated using

how can I turn this array into a proper .opus or .ogg file?

You're capturing a Webm file stream via audio/webm;codecs=opus, so that would need to be converted from Webm to Ogg Opus. Instead, you could simply download or send the Webm Opus stream as a file as-is:

It helps if you create Minimal reproducible example. Also your waveform don't include an important signal clk_counter.

Try this in your testbench, if it doesn't work you at least have the minimum reproducible example.

I changed the initialization of clk_counter and in the increment I simply added 1 instead of 1'b1, if you wanted to be strict you could add a 5-bit wide 1 (5'b1).

TensorFlow can't compute the output shape of your layer. As Conv2D requires a specific shape (4 dimensions), it will fail if the output shape of the previous layer is not known (None).

To fix that, you need to specify which axis you want to squeeze in you call function.

Here, I specify that this is the last axis that need to be squeezed (the channel axis).

You need to keep Rst in the testbench high for a longer amount of time. The 1st posedge of Clk happens at time 200ns, and that is when you release the reset. You need to keep the reset asserted until after the 1st posedge of the clock in order to properly reset your RegFile, since it is a synchronous reset.

This testbench change allows RegFile to be reset to known values: