Runs a fingerprinting algorithm on input music in order to determine the song name and the position in the song.
I take each song that I'd like to add to the database and I run realFFT on each song with a window size of 4096 and a slide value of 2048.
I generate a spectogram like this for each song:
I then store that spectrogram in a songname.fp file and I put it in my database/ directory.
Once createdb.py is finished creating a directory of songs, I can run find.py which will record a several second snippet and store that audio in a file called tmp.wav. I then run fingerprint.py to generate the fp, though this time I don't store it in a file.
Once I have both fingerprints, I run a function in fingerprint.py, def match(fp1, fp2). This takes two fingerprints and sums the absolute difference between fingerprint 1 and fingerprint 2. See the code below for my simple difference function.
def diff(l1, l2):
sum = 0
for i in range(len(l1)):
r = min(len(l1[i]), len(l2[i]))
for j in range(r):
sum += abs(l1[i][j] - l2[i][j])
return sum
I then iterate over the length of fingerprint 1, dragging fingureprint 2 across and computing the difference at each time step, then I append that to an array.
Once I've done the whole song, I take the smallest value in that array and computing the best match time by converting from the index of that sample into time.
I repeat this process for every song until I have an array of (songname, difference_term, time) tuples. I then take the lowest tuple in that array and return that as the best match song to the user.
I'm going to implement constellation hashing similar to what Shazam does to speed up the search process.
I created a small test database of 8 songs and fed in
| Song | Training Duration | Predicted Song |
|---|---|---|
| Alessia_Cara_-_Here.fp | 4 | Alessia_Cara_-_Here |
| Alesso_-Heroes(we_could_be).fp | 4 | Alesso_-Heroes(we_could_be) |
| Andy_Grammer_-_Honey,_I'm_Good..fp | 4 | Cage_The_Elephant_-_Back_Against_the_Wall |
| Big_Sean_-_I_Don't_Fuck_With_You.fp | 4 | Big_Sean_-_I_Don't_Fuck_With_You |
| Big_Wild_-_Aftergold.fp | 4 | Alessia_Cara_-_Here |
| Big_Wild_-Aftergold(feat._Tove_Styrke).fp | 4 | Alessia_Cara_-_Here |
| Cage_The_Elephant_-_Back_Against_the_Wall.fp | 4 | Cage_The_Elephant_-_Back_Against_the_Wall |
| Calvin_Harris_-_Outside.fp | 4 | Calvin_Harris_-_Outside |
First add MP3's to the folder music/mp3. Once you've done that run the script convert.py and make a wav folder to store the converted wav files.
mkdir music/wav
python3 convert.py
Then create your database of samples. This will put computed fingerprints in database/songname.fp.
python3 createdb.py
Then run find.py to start classifying samples.
python3 find.py
Assumes a structure of folders as follows:
database/
songname1.fp
songname2.fp
music/
mp3/
songname1.mp3
songname2.mp3
wav/
songname1.wav
songname2.wav