Feature learning and deep architectures: new directions for music informatics
Journal of Intelligent Information Systems
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Though tags and metadata may provide rich indicators of relationships between high-level concepts like songs, artists or even genres, verbal descriptors lack the fine-grained detail necessary to capture acoustic nuances necessary for efficient retrieval of sounds in extremely large sample libraries. To these ends, we present a flexible approach titled Non-linear Semantic Embedding (NLSE), capable of projecting high-dimensional time-frequency representations of musical instrument samples into a low-dimensional, semantically-organized metric space. As opposed to other dimensionality reduction techniques, NLSE incorporates extrinsic semantic information in learning a projection, automatically learns salient acoustic features, and generates an intuitively meaningful output space.