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An aliasing check when creating CQT filters\r\n2. Changes the CQT oversampling/aggregation hop to be based on the shortest filter rather than the longest filter.\r\n3. Adds 1/N Parseval normalization to CQT output.\r\n4. Adds a `norm` argument to cqt and filters.constant_q in order to allow normalization using a different norm.\r\n5. Does \"sparsification\" on fft_basis in order to exploit sparse ops.  (Without this there's no reason to do the processing in the frequency domain).\r\n\r\nLet me know if any of these are useful.  I'm happy to revert any of them if a subset are useful.  Not sure what kind of lint requirements you have.  Also, I wasn't able to run the tests because I don't have matlab.  Why is Matlab a requirement for a Python library. ;)  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