seewav | Audio waveform visualisation, converts any audio to a nice video | Audio Utils library

yes install it if you have

if you have windows: https://windowsloop.com/install-ffmpeg-windows-10/

if you have ubuntu, linux,etc, on console sudo apt install ffmpeg

if you have mac, on terminal brew install ffmpeg

Regards

Try to call AR function with do.call.

audio is just a curve which wobbles over time where this wobble mirrors your eardrum or microphone pickup membrane ... this signal is in the time domain where axis are time on X and curve height on Y ... typical CD quality audio has 44,100 samples per second meaning you capture that number of points on this audio curve per second ... what gets captured is the audio curve height whereas time is implied knowing each sample is captured in a known sample rate ... so sample rate is one of the two critical audio attributes on digital audio ... bit depth is the other attribute ... if you devote two bytes ( 16 bits ) to record CD quality curve height you get 2 raised to the 16th power ( 2^16 == 65536 ) distinct possible values to store the curve height

its critical to emphasize a raw audio signal is in the time domain (X is time Y is curve height) ... when you send a set of these samples into a fft call the data gets transformed into the frequency domain (X is frequency Y is magnitude [energy]) so the direct dimension of time is gone yet is baked into the notion of that entire body of frequency domain data ... there are trade offs when deciding both the number of samples you feed into the fft call ( sample window size ) namely to increase the frequency resolution of the freq domain signal (to lower incr_freq ) you need more audio samples to get fed into the fft call however to gain temporal specificity in the freq domain you need as few samples as possible which you pay for by getting a lower frequency resolution and lower peak freq ( lower nyquist limit )

to generate a spectrogram you feed a memory buffer of say 4096 samples of this curve height array ( time domain ) into a Fourier Transform ( fft ) which will return back an array ( freq domain ) of same number of array elements yet this time each element stores a complex number from which you can calculate the magnitude ( energy level ) and phase ... array element zero is the DC bias which can be ignored ... each array element represents a distinct frequency where the freq increment can be calculated

with sample_rate of 44100 samples per second, and one second worth of samples ( 44100 )
this gives you a frequency increment resolution of 1 hertz ... IE each freq bin is 1 Hertz apart

Here's an example using lapply.