CoverSongSimilarity¶

standard mode | Music Similarity category

Inputs¶

inputArray (vector_vector_real) - a 2D binary cross-similarity matrix between two audio chroma vectors (query vs reference song) (refer ‘ChromaCrossSimilarity’ algorithm’).

Outputs¶

scoreMatrix (vector_vector_real) - a 2D smith-waterman alignment score matrix from the input binary cross-similarity matrix

distance (real) - cover song similarity distance between the query and reference song from the input similarity matrix. Either ‘asymmetric’ (as described in [2]) or ‘symmetric’ (maximum score in the alignment score matrix).

Parameters¶

alignmentType (string ∈ {serra09, chen17}, default = serra09) :
choose either one of the given local-alignment constraints for smith-waterman algorithm as described in [2] or [3] respectively.

disExtension (real ∈ [0, ∞), default = 0.5) :
penalty for disruption extension

disOnset (real ∈ [0, ∞), default = 0.5) :
penalty for disruption onset

distanceType (string ∈ {asymmetric, symmetric}, default = asymmetric) :
choose the type of distance. By default the algorithm outputs a asymmetric distance which is obtained by normalising the maximum score in the alignment score matrix with length of reference song

Description¶

This algorithm computes a cover song similiarity measure from a binary cross similarity matrix input between two chroma vectors of a query and reference song using various alignment constraints of smith-waterman local-alignment algorithm.

This algorithm expects to recieve the binary similarity matrix input from essentia ‘ChromaCrossSimilarity’ algorithm or essentia ‘CrossSimilarityMatrix’ with parameter ‘binarize=True’.

The algorithm provides two different allignment contraints for computing the smith-waterman score matrix (check references).

Exceptions are thrown if the input similarity matrix is not binary or empty.

References:

[1] Smith-Waterman algorithm (Wikipedia, https://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm).

[2] Serra, J., Serra, X., & Andrzejak, R. G. (2009). Cross recurrence quantification for cover song identification.New Journal of Physics.

[3] Chen, N., Li, W., & Xiao, H. (2017). Fusing similarity functions for cover song identification. Multimedia Tools and Applications.

Source code¶

C++ source code

C++ header file