Algorithms for the discovery of musical repetition have been developed in audio and symbolic domains more or less independently for over a decade. In this paper we combine algorithms for multiple F0 estimation, beat tracking, quantisation, and pattern discovery, so that for the first time, the note content of motifs, themes, and repeated sections can be discovered directly from polyphonic music...

A cellular network of associative processors capable of symbolic processing is described in this paper. The processors are placed in a two dimensional array and they can perform a global set of symbolic rules deening their next state and the messages to be passed to their neighbours. The system follows a cellular automata like operation and the aim is to transform initial symbolic descriptions ...

In this paper we propose refinements for optimal search with symbolic pattern databases in deterministic statespace planning. As main memory is limited, external heuristic search is combined with the power of symbolic representation. We start with an external version of symbolic breadth-first search. Then an alternative and external implementation for BDDA* to include different heuristic evalua...

In this research article, we apply clustering within the Symbolic Pattern Recognition (SPR) framework to problems related to classifying different clinical categories of atrial fibrillation by modeling the changes in electrical activity of the heart. SPR characterizes a sequential dataset by modeling the transition behavior exhibited by patterns of symbols; clearly, this technique requires cont...

Concept lattices are mathematical structures useful for many tasks in knowledge discovery and management. A concept lattice is basically obtained from binary data encoding the membership of some attributes to some objects. Dealing with complex data brings the important problem of discretization and the associated loss of information. To avoid discretization, (i) pattern structures and (ii) symb...

This paper proposes a symbolic pattern retrieval method using emotional feature vectors. Queries and symbolic patterns are represented by emotional vectors composed of eight numerical parameters. Since the proposed method uses numerical vectors close to raw data instead of recognized text, the information loss by data conversion is small. This point is advantageous compared with conventional te...

Symbolic search with BDDs has shown remarkable performance for cost-optimal deterministic planning by exploiting a succinct representation and exploration of state sets. In this paper we enhance BDD-based planning by applying a combination of domain-independent search techniques: the optimization of the variable ordering in the BDD by approximating the linear arrangement problem, pattern select...

We present a novel technique for combining statistical machine learning for proof-pattern recognition with symbolic methods for lemma discovery. The resulting tool, ACL2(ml), gathers proof statistics and uses statistical pattern-recognition to pre-processes data from libraries, and then suggests auxiliary lemmas in new proofs by analogy with already seen examples. This paper presents the implem...

This paper studies the impact of pattern databases for solving shortest path planning problems with limited memory. It contributes a bucket implementation of Dijkstra’s algorithm for the construction of shortest path planning pattern databases and their use in symbolic A* search. For improved efficiency, the paper analyzes the locality for weighted problem graphs and shows that it matches the d...