On the Importance of Temporal Context in Proximity Kernels: A Vocal Separation Case Study
| Autor: | Yela, D. F., Sebastian Ewert, Fitzgerald, D., Sandler, M. |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Scopus-Elsevier |
| Popis: | Musical source separation methods exploit source-specific spectral characteristics to facilitate the decomposition process. Kernel Additive Modelling (KAM) models a source applying robust statistics to time-frequency bins as specified by a source-specific kernel, a function defining similarity between bins. Kernels in existing approaches are typically defined using metrics between single time frames. In the presence of noise and other sound sources information from a single-frame, however, turns out to be unreliable and often incorrect frames are selected as similar. In this paper, we incorporate a temporal context into the kernel to provide additional information stabilizing the similarity search. Evaluated in the context of vocal separation, our simple extension led to a considerable improvement in separation quality compared to previous kernels. 2017 AES International Conference on Semantic Audio |
| Databáze: | OpenAIRE |
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