A subsequence matching with gaps-range-tolerances framework

Abstract

We propose a novel subsequence matching framework that allows for gaps in both the query and target sequences, variable matching tolerance levels efficiently tuned for each query and target sequence, and also constrains the maximum match length. Using this framework, a space and time efficient dynamic programming method is developed: given a short query sequence and a large database, our method identifies the subsequence of the database that best matches the query, and further bounds the number of consecutive gaps in both sequences. In addition, it allows the user to constrain the minimum number of matching elements between a query and a database sequence. We show that the proposed method is highly applicable to music retrieval. Music pieces are represented by 2-dimensional time series, where each dimension holds information about the pitch and duration of each note, respectively. At runtime, the query song is transformed to the same 2-dimensional representation. We present an extensive experimental evaluation using synthetic and hummed queries on a large music database. Our method outperforms, in terms of accuracy, several DP-based subsequence matching methods---with the same time complexity---and a probabilistic model-based method.