A method of identifying an optimised set of filters for use in generating an acoustic fingerprint, the method comprising: i) providing one or more databases (351, 353) comprising a plurality of audio samples including N M matched pairs of audio samples and N N non-matched pairs of audio samples, each matched pair of audio samples comprising an original audio sample and a distorted version of a same original audio signal and each non-matched pair of audio samples comprising an original audio sample and a version of a different original audio signal; ii) determining (s5, s7, s25, s27) a spectrogram for each of the plurality of audio samples in the one or more databases; iii) applying (s9, s11, s29, s31) each of N f candidate filters to the spectrograms and binarising (361, 363, 369,371) a result to generate a plurality of vectors of binary bits, each vector of binary bits being associated with one candidate filter and one audio sample; iv) comparing the binary bits in the vectors associated with a selected matched pair of audio samples for a current filter to determine bit error rate information for the current filter and the selected matched pair of audio samples; v) repeating step iv) for each matched pair of audio samples to determine mean and variance information for the bit error rate information determined in step iv) for the current filter and the matched pairs of audio samples; vi) comparing the binary bits in the vectors associated with a selected non-matched pair of audio samples for the current filter to determine bit error rate information for the current filter and the selected non-matched pair of audio samples; vii) repeating step vi) for each non-matched pair of audio samples to determine mean and variance information for the bit error rate information determined in step vi) for the current filter and the non-matched pairs of audio samples; viii) repeating steps iv) to vii) for each candidate filter to determine mean and variance information for each candidate filter for the matched pairs of audio samples and to determine mean and variance information for each candidate filter for the non-matched pairs of audio samples; and ix) determining, using a dynamic programming optimisation technique, a subset of said candidate filters as said optimised set of filters for use in generating an acoustic fingerprint using the determined mean and variance information for each candidate filter for the matched pairs of audio samples and the determined mean and variance information for each candidate filter for the non-matched pairs of audio samples. i) providing one or more databases (351, 353) comprising a plurality of audio samples including N M matched pairs of audio samples and N N non-matched pairs of audio samples, each matched pair of audio samples comprising an original audio sample and a distorted version of a same original audio signal and each non-matched pair of audio samples comprising an original audio sample and a version of a different original audio signal; ii) determining (s5, s7, s25, s27) a spectrogram for each of the plurality of audio samples in the one or more databases; iii) applying (s9, s11, s29, s31) each of N f candidate filters to the spectrograms and binarising (361, 363, 369,371) a result to generate a plurality of vectors of binary bits, each vector of binary bits being associated with one candidate filter and one audio sample; iv) comparing the binary bits in the vectors associated with a selected matched pair of audio samples for a current filter to determine bit error rate information for the current filter and the selected matched pair of audio samples; v) repeating step iv) for each matched pair of audio samples to determine mean and variance information for the bit error rate information determined in step iv) for the current filter and the matched pairs of audio samples; vi) comparing the binary bits in the vectors associated with a selected non-matched pair of audio samples for the current filter to determine bit error rate information for the current filter and the selected non-matched pair of audio samples; vii) repeating step vi) for each non-matched pair of audio samples to determine mean and variance information for the bit error rate information determined in step vi) for the current filter and the non-matched pairs of audio samples; viii) repeating steps iv) to vii) for each candidate filter to determine mean and variance information for each candidate filter for the matched pairs of audio samples and to determine mean and variance information for each candidate filter for the non-matched pairs of audio samples; and ix) determining, using a dynamic programming optimisation technique, a subset of said candidate filters as said optimised set of filters for use in generating an acoustic fingerprint using the determined mean and variance information for each candidate filter for the matched pairs of audio samples and the determined mean and variance information for each candidate filter for the non-matched pairs of audio samples.