Abstract
We introduce a novel application of genetic motif discovery in symbolic sequence representations of sound for audio event detection. Sounds are represented as a set of parallel symbolic sequences, each symbol representing a spectral shape, and each layer indicating the contribution weights of each spectral shape to the sound. Such layered symbolic representations are input to a genetic motif discovery algorithm that detects and clusters recurrent and structurally salient sound events in an unsupervised and query less manner. The found motifs can be interpreted as statistical temporal models of spectral evolution. The system is successfully evaluated in two tasks: environmental sound event detection, and drum onset detection.
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