meyda | Audio feature extraction for JavaScript | Audio Utils library

TL;DR Amplitude Spectrum is basically FFT of the signal, and Power Spectrum is a squared value of the Amplitude Spectrum, which is also referred as energy sometimes. Here is one of examples from Meyda website that is calculating Amplitude Spectrum https://github.com/catalli/audiotrainer-server/blob/df41322906c88cd6f899e8f9b9661ebb949f72e1/index.js#L17

Long answer:

Now, lets look into your code sample line by line and figure out what is it doing and how to implement it in javascript.

1. S = numpy.abs(librosa.stft(y=audio_sample, n_fft=1024, hop_length=500)) ** 2

this is calculating square values of 1024 bins fft of audio_sample y, which is basically a Power Spectrum or an Amplitude Spectrum squared. Please note that the abs of complex number is a vector lenth: sqrt(real_part^2 + img_part^2)

1. mel_S = numpy.dot(librosa.filters.mel(sr=44100, n_fft=1024, n_mels=64), S).T

this is an mfcc calculation, which is basically a product of predefined filter banks and fft squared.

1. log_mel_S = librosa.power_to_db(mel_S, ref=1.0, amin=1e-10, top_db=None)

this last one will convert the result to decibel (dB) units (10 * log10(S / ref))

i will extend this answer with js code-sample later, submitting it now because i think it will be helpful already as it is