Home > Programming, Windows > Audio FingerPrinting and Matching Using Acoustid Chromaprint on Windows With Python

Audio FingerPrinting and Matching Using Acoustid Chromaprint on Windows With Python

I needed to generate audio fingerprints for matching/pattern recognition. I should say I tried a couple of approaches like using MFCC and spectrogram matching using Computer Vision techniques but I was not successfully with it. Using acoustid Chromaprint gave positive results. Since I was using windows, I had to compile the latest version using Visual Studio.
Since I my target was to get wav format to work, I didnt need ffmpeg dependency, so I removed it. You will need the avcodec and ffwt libs. The algorithm used for Acoustid for finger print matching is buried in a PostgreSQL user-defined C function which I easily translated to python for use.

In the chromaprint python module, I just need to add only one function that compares two fingerprints and returns the distance between them. In the original function, a score is returned, the only change I return 1-score, which I call the distance measure.
Following is the distance measure function.
def calculate_distance(fingerprint1, fingerprint2):
    '''  http://oxygene.sk/lukas/2011/01/how-does-chromaprint-work/ 
    This algorithm is extracted from the postgres chromaprint c function
    Instead of returning the score
    I return 1-score as the distance measure
    '''
    if len(numpy.shape(fingerprint1)) != 1 != len(numpy.shape(fingerprint2)):
        raise Exception('Only one dimension arrays allowed')
    if fingerprint1 == None or fingerprint2 == None:
        return 0
    
    numcounts = len(fingerprint1) + len(fingerprint2) + 1;
    counts = numpy.zeros(numcounts);
    for i in range(len(fingerprint1)):        
        jbegin = max(0, i - ACOUSTID_MAX_ALIGN_OFFSET);
        jend = min(len(fingerprint2), i + ACOUSTID_MAX_ALIGN_OFFSET);
        for j in range(jbegin,jend):
            biterror = popcount_lookup8(fingerprint1[i] ^ fingerprint2[j]);
            # ereport(DEBUG5, (errmsg("comparing %d and %d with error %d", i, j, biterror))); 
            if (biterror <= ACOUSTID_MAX_BIT_ERROR):
                offset = i - j + len(fingerprint2)
                counts[offset] += 1
                
    return 1.0 - (numpy.max(counts) /(1.0* min(len(fingerprint1), len(fingerprint2))))
Depending on the what you target of using the chromaprint matching algorithm, you can adjust the ACOUSTID_MAX_BIT_ERROR, and ACOUSTID_MAX_ALIGN_OFFSET to get lower or high distance values that you can use as the basis later on for classification using K-Nearest Neighbour. You can download the windows chromaprint version 0.4 here. The download I compiled, fingerprints only wave data since I removed ffmpeg dependencies. The download also has the dependency dlls.
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  1. J
    June 23, 2011 at 11:32 pm

    What function is popcount_lookup8?

  2. Joseph Ssenyange
    August 9, 2011 at 1:41 pm

    popcount_table_8bit = [\
    0,1,1,2,1,2,2,3,1,2,2,3,2,3,3,4,1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,\
    1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,\
    1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,\
    2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,\
    1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,\
    2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,\
    2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,\
    3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,4,5,5,6,5,6,6,7,5,6,6,7,6,7,7,8,\
    ];

    def popcount_lookup8(x):
    global popcount_table_8bit
    return popcount_table_8bit[x & 255] + \
    popcount_table_8bit[(x >> 8) & 255] + \
    popcount_table_8bit[(x >> 16) & 255] + \
    popcount_table_8bit[(x >> 24)]

  3. Alix Axel
    July 5, 2012 at 4:55 am

    Which is basically the total number of 1’s in the binary representation of x, right?

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