Audio landmark-based fingerprinting.
Create a new fingerprint dbase with new,
append new files to an existing database with add,
merge fingerprint databases into another with merge,
precompute per-file fingerprints with precompute,
or identify noisy query excerpts with match.
Usage: audfprint (new | add | merge | precompute | match) [-d <dbase> | --dbase <dbase>] [options] <file>...
Options:
-n <dens>, --density <dens> Target hashes per second [default: 20.0]
-h <bits>, --hashbits <bits> How many bits in each hash [default: 20]
-b <val>, --bucketsize <val> Number of entries per bucket [default: 100]
-t <val>, --maxtime <val> Largest time value stored [default: 16384]
-r <val>, --samplerate <val> Resample input files to this [default: 11025]
-p <dir>, --precompdir <dir> Save precomputed files under this dir [default: .]
-i <val>, --shifts <val> Use this many subframe shifts building fp [default: 0]
-w <val>, --match-win <val> Maximum tolerable frame skew to count as a match [default: 1]
-N <val>, --min-count <val> Minimum number of matching landmarks to count as a match [default: 5]
-x <val>, --max-matches <val> Maximum number of matches to report for each query [default: 1]
-S <val>, --freq-sd <val> Frequency peak spreading SD in bins [default: 30.0]
-F <val>, --fanout <val> Max number of hash pairs per peak [default: 3]
-P <val>, --pks-per-frame <val> Maximum number of peaks per frame [default: 5]
-H <val>, --ncores <val> Number of processes to use [default: 1]
-o <name>, --opfile <name> Write output (matches) to this file, not stdout [default: ]
-l, --list Input files are lists, not audio
-T, --sortbytime Sort multiple hits per file by time (instead of score)
-v <val>, --verbose <val> Verbosity level [default: 1]
-I, --illustrate Make a plot showing the match
-W <dir>, --wavdir <dir> Find sound files under this dir [default: ]
--version Report version number
--help Print this message
Uses librosa, get https://github.com/bmcfee/librosa or "pip install librosa"
Uses docopt, see http://docopt.org , get https://github.com/docopt/docopt or "pip install docopt"
Also uses numpy and scipy, which need to be installed, e.g. "pip install numpy", "pip install scipy".
Parallel processing relies on joblib module (and multiprocessing module), which are likely already installed.
Based on Matlab prototype, http://www.ee.columbia.edu/~dpwe/resources/matlab/audfprint/ . This python code will actually read and use databases created by the Matlab code (version 0.90 upwards).
Build a database of fingerprints from a set of reference audio files:
> python audfprint.py new --dbase fpdbase.pklz Nine_Lives/0*.mp3
Wed Sep 10 10:52:18 2014 ingesting #0:Nine_Lives/01-Nine_Lives.mp3 ...
Wed Sep 10 10:52:20 2014 ingesting #1:Nine_Lives/02-Falling_In_Love.mp3 ...
Wed Sep 10 10:52:22 2014 ingesting #2:Nine_Lives/03-Hole_In_My_Soul.mp3 ...
Wed Sep 10 10:52:25 2014 ingesting #3:Nine_Lives/04-Taste_Of_India.mp3 ...
Wed Sep 10 10:52:28 2014 ingesting #4:Nine_Lives/05-Full_Circle.mp3 ...
Wed Sep 10 10:52:31 2014 ingesting #5:Nine_Lives/06-Something_s_Gotta_Give.mp3 ...
Wed Sep 10 10:52:32 2014 ingesting #6:Nine_Lives/07-Ain_t_That_A_Bitch.mp3 ...
Wed Sep 10 10:52:35 2014 ingesting #7:Nine_Lives/08-The_Farm.mp3 ...
Wed Sep 10 10:52:37 2014 ingesting #8:Nine_Lives/09-Crash.mp3 ...
Added 63241 hashes (24.8 hashes/sec)
Processed 9 files (2547.3 s total dur) in 21.6 s sec = 0.008 x RT
Saved fprints for 9 files ( 63241 hashes) to fpdbase.pklz
Add more reference tracks to an existing database:
> python audfprint.py add --dbase fpdbase.pklz Nine_Lives/1*.mp3
Read fprints for 9 files ( 63241 hashes) from fpdbase.pklz
Wed Sep 10 10:53:14 2014 ingesting #0:Nine_Lives/10-Kiss_Your_Past_Good-bye.mp3 ...
Wed Sep 10 10:53:16 2014 ingesting #1:Nine_Lives/11-Pink.mp3 ...
Wed Sep 10 10:53:18 2014 ingesting #2:Nine_Lives/12-Attitude_Adjustment.mp3 ...
Wed Sep 10 10:53:20 2014 ingesting #3:Nine_Lives/13-Fallen_Angels.mp3 ...
Added 27067 hashes (22.0 hashes/sec)
Processed 4 files (1228.6 s total dur) in 13.0 s sec = 0.011 x RT
Saved fprints for 13 files ( 90308 hashes) to fpdbase.pklz
Match a fragment recorded of music playing in the background against the database:
> python audfprint.py match --dbase fpdbase.pklz query.mp3
Read fprints for 13 files ( 90308 hashes) from fpdbase.pklz
Analyzed query.mp3 of 5.573 s to 204 hashes
Matched query.mp3 5.573 sec 204 raw hashes as Nine_Lives/05-Full_Circle.mp3 at 50.085 s with 8 of 9 hashes
Processed 1 files (5.8 s total dur) in 2.6 s sec = 0.443 x RT
The query contained audio from Nine_Lives/05-Full_Circle.mp3 starting at 50.085 sec into the track. There were a total of 17 landmark hashes shared between the query and that track, and 14 of them had a consistent time offset. Generally, anything more than 5 or 6 consistently-timed matching hashes indicate a true match, and random chance will result in fewer than 1% of the raw common hashes being temporally consistent.
The fingerprint database records 2^20 (~1M) distinct fingerprints, with (by default) 100 entries for each fingerprint bucket. When the bucket fills, track entries are dropped at random; since matching depends only on making a minimum number of matches, but no particular match, dropping some of the more popular ones does not prevent matching. The Matlab version has been successfully used for databases of 100k+ tracks. Reducing the hash density (--density) leads to smaller reference database size, and the capacity to record more reference items before buckets begin to fill; a density of 7.0 works well.
Times (in units of 256 samples, i.e., 23 ms at the default 11kHz sampling rate) are stored in the bottom 14 bits of each database entry, meaning that times larger than 2^14*0.023 = 380 sec, or about 6 mins, are aliased. If you want to correctly identify time offsets in tracks longer than this, you need to use a larger --maxtime. The trade-off is that the remaining bits in each 32 bit entry (i.e., 18 bits for the default 14 bit times) are used to store the track ID. Thus, by default, the database can only remember 2^18 = 262k tracks; using a larger --maxtime will reduce this; similarly, you can increase the number of distinct tracks by reducing --maxtime, which doesn't prevent matching tracks, but progressively reduces discrimination as the number of distinct time slots reduces (and makes the reported time offsets inaccurate for longer tracks).