Abstract
An active-period-aware supervised nonnegative matrix factorization (NMF) approach for music transcription is proposed. Supervised NMF relies on a set of known spectrograms associated with all musical instruments that may possibly be involved with given music data; this is supported by the availability of large database of a variety of musical instruments. It is free from the source-number determination problem and this is a significant advantage over the unsupervised NMF approaches. The proposed approach is composed of three steps. Step 1: Apply the existing supervised NMF algorithm. Step 2: Estimate the 'active' periods (during which musical sounds are present) based on the outcomes of Step 1. Step 3: Optimize a refined cost function reflecting the estimate of active periods. The awareness of active periods leads to avoidance of the so-called octave-errors which is a central issue of the existing supervised NMF method. Simulation results show the efficacy of the proposed approach.1
| Original language | English |
|---|---|
| Title of host publication | 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 14-18 |
| Number of pages | 5 |
| ISBN (Electronic) | 9789881476807 |
| DOIs | |
| Publication status | Published - 2016 Feb 19 |
| Event | 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong Duration: 2015 Dec 16 → 2015 Dec 19 |
Other
| Other | 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 |
|---|---|
| Country | Hong Kong |
| City | Hong Kong |
| Period | 15/12/16 → 15/12/19 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Modelling and Simulation
- Signal Processing
Cite this
Supervised nonnegative matrix factorization using active-period-aware structured l1-norm for music transcription. / Morikawa, Yu; Yukawa, Masahiro; Kikuchi, Hisakazu.
2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 14-18 7415510.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Supervised nonnegative matrix factorization using active-period-aware structured l1-norm for music transcription
AU - Morikawa, Yu
AU - Yukawa, Masahiro
AU - Kikuchi, Hisakazu
PY - 2016/2/19
Y1 - 2016/2/19
N2 - An active-period-aware supervised nonnegative matrix factorization (NMF) approach for music transcription is proposed. Supervised NMF relies on a set of known spectrograms associated with all musical instruments that may possibly be involved with given music data; this is supported by the availability of large database of a variety of musical instruments. It is free from the source-number determination problem and this is a significant advantage over the unsupervised NMF approaches. The proposed approach is composed of three steps. Step 1: Apply the existing supervised NMF algorithm. Step 2: Estimate the 'active' periods (during which musical sounds are present) based on the outcomes of Step 1. Step 3: Optimize a refined cost function reflecting the estimate of active periods. The awareness of active periods leads to avoidance of the so-called octave-errors which is a central issue of the existing supervised NMF method. Simulation results show the efficacy of the proposed approach.1
AB - An active-period-aware supervised nonnegative matrix factorization (NMF) approach for music transcription is proposed. Supervised NMF relies on a set of known spectrograms associated with all musical instruments that may possibly be involved with given music data; this is supported by the availability of large database of a variety of musical instruments. It is free from the source-number determination problem and this is a significant advantage over the unsupervised NMF approaches. The proposed approach is composed of three steps. Step 1: Apply the existing supervised NMF algorithm. Step 2: Estimate the 'active' periods (during which musical sounds are present) based on the outcomes of Step 1. Step 3: Optimize a refined cost function reflecting the estimate of active periods. The awareness of active periods leads to avoidance of the so-called octave-errors which is a central issue of the existing supervised NMF method. Simulation results show the efficacy of the proposed approach.1
UR - http://www.scopus.com/inward/record.url?scp=84986208159&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84986208159&partnerID=8YFLogxK
U2 - 10.1109/APSIPA.2015.7415510
DO - 10.1109/APSIPA.2015.7415510
M3 - Conference contribution
SP - 14
EP - 18
BT - 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -