I am developing an application that requires audio fingerprints. I have been reading a lot of articles and PDFs, now i think i have gotten myself confused. Based on my present understanding, I have some questions
After decoding the audio to its raw format, resampling and extracting the mono channel. Do i convert the bytes to integers or floats?
What is convolution and why is it necessary to convolve the audio samples
What is a window and its length
FFT transforms a signal from time domain to frequency domain. Am i correct? If yes, does the frequency components of an audio determine the contents/sound/noise/volume?
What operation can be carried out on two audios of same content but different bitrate to normalize the bitrate and get the data that they share
What are low pass and high pass filters and how are the derived. What are their uses in relation to audio fingerprint operations
In the text Computer vision for music identification. After convolving the signals with a low pass filter and extracting every 8th sample. Then, a short term fourier transform with a window size of 2048 samples with successive windows offset by 64 samples. Also divide the power between 300Hz and 2000Hz in 33 logarithmically spaced bands.
- Will these operations be applied to each of the samples retrieved after the convolution process? Can i get a simpler explanation?
32 learned filters and thresholds are applied to get a 32-bit descriptor for every time step (11.6 ms) of signal. This series of signals are known as the signature.
- what are these learned filters and thresholds?
Wow :), that's a lot of questions. I am sure a lot of beginners will find the answers helpful.