Multi-pitch estimation via fast group sparse learning
| Autor: | Filip Elvander, Andreas Jakobsson, Ted Kronvall, Stefan Ingi Adalbjörnsson |
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| Rok vydání: | 2016 |
| Předmět: |
Signal processing
Mathematical optimization Optimization problem Computational complexity theory 020206 networking & telecommunications 02 engineering and technology 030507 speech-language pathology & audiology 03 medical and health sciences Computer Science::Sound Convergence (routing) Convex optimization 0202 electrical engineering electronic engineering information engineering Redundancy (engineering) 0305 other medical science Representation (mathematics) Heuristics Algorithm Mathematics |
| Zdroj: | EUSIPCO |
| DOI: | 10.1109/eusipco.2016.7760417 |
| Popis: | In this work, we consider the problem of multi-pitch estimation using sparse heuristics and convex modeling. In general, this is a difficult non-linear optimization problem, as the frequencies belonging to one pitch often overlap the frequencies belonging to other pitches, thereby causing ambiguity between pitches with similar frequency content. The problem is further complicated by the fact that the number of pitches is typically not known. In this work, we propose a sparse modeling framework using a generalized chroma representation in order to remove redundancy and lower the dictionary's block-coherency. The found chroma estimates are then used to solve a small convex problem, whereby spectral smoothness is enforced, resulting in the corresponding pitch estimates. Compared with previously published sparse approaches, the resulting algorithm reduces the computational complexity of each iteration, as well as speeding up the overall convergence. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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