Abstract
In this work, we propose a time-recursive multi-pitch estimation algorithm, using a sparse reconstruction framework, assuming
only a few pitches from a large set of candidates to be active at each time instant. The proposed algorithm utilizes a sparse
recursive least squares formulation augmented by an adaptive penalty term specifically designed to enforce a pitch structure on
the solution. When evaluated on a set of ten music pieces, the proposed method is shown to outperform state-of-the-art multi-
pitch estimators in either accuracy or computational spe
only a few pitches from a large set of candidates to be active at each time instant. The proposed algorithm utilizes a sparse
recursive least squares formulation augmented by an adaptive penalty term specifically designed to enforce a pitch structure on
the solution. When evaluated on a set of ten music pieces, the proposed method is shown to outperform state-of-the-art multi-
pitch estimators in either accuracy or computational spe
Original language | English |
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Title of host publication | 50th Asilomar Conference on Signals, Systems, and Computers, 2016 |
Publisher | IEEE - Institute of Electrical and Electronics Engineers Inc. |
Pages | 369-373 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-5386-3954-2 |
DOIs | |
Publication status | Published - 2017 |
Event | 50th Annual Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2016) - Asilomar Hotel & Conference Grounds, Pacific Grove, United States Duration: 2016 Nov 6 → 2016 Nov 9 |
Conference
Conference | 50th Annual Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2016) |
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Country/Territory | United States |
City | Pacific Grove |
Period | 2016/11/06 → 2016/11/09 |
Subject classification (UKÄ)
- Probability Theory and Statistics
- Signal Processing