A new approach to text searching
Communications of the ACM
Text algorithms
A fast string searching algorithm
Communications of the ACM
Distributed pattern matching using finite automata
Journal of Automata, Languages and Combinatorics
A Bit-Parallel Approach to Suffix Automata: Fast Extended String Matching
CPM '98 Proceedings of the 9th Annual Symposium on Combinatorial Pattern Matching
On minimizing pattern splitting in multi-track string matching
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
Searching notated polyphonic music using transportation distances
Proceedings of the 12th annual ACM international conference on Multimedia
Interval Distinction on Melody Perception for Music Information Retrieval
Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music
Searching for music: from melodies in mind to the resources on the web
Proceedings of the 13th International Conference on Humans and Computers
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We consider the problem of multi-track string matching. The task is to find the occurrences of a pattern across parallel strings. Given an alphabet Σ of natural numbers and a set S over Σ of h strings si = si1 ... sinn for i =1,...,h a pattern p = p1...pm has such an occurrence at position j of S if p1 = si1j, p2 = si2j+1,...,pm = simj+m-1 holds for i1,.....,im ∈ {1 ..... h}. An application of the problem is music retrieval where occurrences of a monophonic query pattern are searched in a polyphonic music database. In music retrieval it is even more pertinent to allow invariance for pitch level transpositions, i.e., the task is to find whether there are occurrences of p in S such that the formulation above becomes p1 = si1j + c, p2 = si2j+1 + c,....., pm= Simj+m-1 + c for some constant c. We present several algorithms solving the problem. Our main contribution, the MONOPOLY algorithm, is a transposition-invariant bit-parallel filtering algorithm for static databases. After an O(nhe) time preprocessing, it finds candidates for transposition invariant occurrences in time O(n⌈m/w⌉ + m + d) where w, e, and d denote the size of the machine word in bits and two factors dependent on the size of the alphabet, respectively. A straightforward algorithm is used to check whether the candidates are proper occurrences. The algorithm needs time O(hm) per candidate.