Method and system for robust audio hashing
Method and system for channel-invariant robust audio hashing is provided with a robust hash extraction step where a robust hash is extracted from audio content dividing the audio content in frames; applying a transformation procedure on the frames to compute, for each frame, transformed coefficients; applying a normalization procedure on the transformed coefficients to obtain normalized coefficients, where the normalization procedure computes the product of the sign of each coefficient of the transformed coefficients by an amplitude-scaling-invariant function of any combination of the transformed coefficients; applying a quantization procedure on the normalized coefficients to obtain the robust hash of the audio content; and a comparison step where the robust hash is compared with reference hashes to find a match.
1. A method for robust audio hashing, comprising a robust hash extraction step wherein a robust hash is extracted from audio content; the robust hash extraction step comprising:
dividing the audio content in at least one frame;
applying a transformation procedure on said at least one frame to compute, for each frame, a plurality of transformed coefficients;
applying a normalization procedure on the transformed coefficients to obtain a plurality of normalized coefficients, wherein said normalization procedure comprises computing the product of the sign of each coefficient of said transformed coefficients by the quotient of two homogeneous functions of any combination of said transformed coefficients, wherein both homogeneous functions are of the same order;
applying a quantization procedure on said normalized coefficients to obtain the robust hash of the audio content.
2. The method according to claim 1 , further comprising a comparison step wherein the robust hash is compared with at least one reference hash to find a match.
3. The method according to claim 2 , wherein the comparison step comprises, for each reference hash:
extracting from the corresponding reference hash at least one sub-hash with the same length J as the length of the robust hash;
converting the robust hash and each of said at least one sub-hash into the corresponding reconstruction symbols given by the quantizer;
computing a similarity measure according to the normalized correlation between the robust hash and each of said at least one sub-hash according to the following rule:
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where h q represents the robust hash of lengh J, h r a reference sub-hash of the same length J, and
where
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comparing a function of said at least one similarity measure against a predefined threshold;
deciding, based on said comparison, whether the robust hash and the reference hash represent the same audio content.
4. The method according to claim 1 , wherein the normalization procedure is applied on the transformed coefficients arranged in a matrix of size F×T to obtain a matrix of normalized coefficients of size F′×T′, with F′=F, T′≦T, whose elements Y(f′t′) are computed according to the following rule:
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where X(f′, M(t′)) are the elements of the matrix of transformed coefficients ( 208 ), X f′ is the fth row of the matrix of transformed coefficients, M( ) is a function that maps indices from { 1 , . . . , T′} to { 1 , . . . , T}, and both H( ) and G( ) are homogeneous functions of the same order.
5. The method according to claim 4 , wherein homogeneous functions H( ) and G( ) are such that:
H ( X f′ )= H ( X f′,M(t′) ), G ( X f′ ) =G ( X f′,M(t′) ),
with
X f′,M(t′) =[X(f′,M(t′)), X(f′,M(t′)+1), . . . , X(f′,k u )],
X f′,M(t′) =[X(f′,k l ), . . . , X(f′,M(t′)−2), X(f′,M(t′)−1], where k l is the maximum of {M(t′)−L l ,1}, k u is the minimum of {M(t′)+L u −1,T}, M(t′)>1, and L l >1, L u >0.
6. The method according to claim 5 , wherein M(t′)=t′+1 and H( X f′,M(t′) )=abs(X(f′,t′+1)), resulting in the following normalization rule:
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7. The method according to claim 6 , wherein
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where L l =L, a=[a( 1 1 , a( 2 ), . . . , a(L)] is a weighting vector and p is a positive real number.
8. The method according to claim 1 , wherein the transformation procedure comprises a spectral subband decomposition of each frame.
9. The method according to claim 1 , wherein in the quantization procedure at least one multilevel quantizer is employed.
10. The method according to claim 9 , wherein the at least one multilevel quantizer is obtained by a training method comprising:
computing partition, obtaining Q disjoint quantization intervals by maximizing a predefined cost function which depend on the statistics of a plurality of normalized coefficients computed from a training set of training audio fragments; and
computing symbols, associating one symbol to each interval computed.
11. The method according to claim 10 , wherein the cost function is the empirical entropy of the quantized coefficients, computed according to the following formula:
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where N i,f is the number of coefficients of the fth row of the matrix of postprocessed coefficients assigned to the ith interval of the partition, and L c is the length of each row.
12. A method for deciding whether two robust hashes computed according to the method for robust audio hashing of claim 1 represent the same audio content, wherein said method comprises:
extracting from the longest hash at least one sub-hash with the same length J as the length of the shortest hash;
converting the shortest hash and each of said at least one sub-hash into the corresponding reconstruction symbols given by the quantizer;
computing a similarity measure according to the normalized correlation between the shortest hash and each of said at least one sub-hash according to the following rule:
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q
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where h q represents the query hash of lengh J, h r a reference sub-hash of the same length J, and where
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comparing a function of said at least one similarity measure against a predefined threshold;
deciding, based on said comparison, whether the two robust hashes represent the same audio content.