Non-Linear Semantic Embedding for Organizing Large Instrument Sample Libraries
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....
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| creator | Humphrey, E. J. Glennon, A. P. Bello, J. P. |
| description | 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. |
| doi_str_mv | 10.1109/ICMLA.2011.105 |
| format | Conference Proceeding |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Aerospace electronics Convolution Instruments Organizations Principal component analysis Semantics Training |
| title | Non-Linear Semantic Embedding for Organizing Large Instrument Sample Libraries |
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