Abstract
This work proposes a lexicalized probabilistic context free grammar designed for meter detection, an integral component of automatic music transcription. The grammar uses rhythmic cues to align a given musical piece with learned metrical stress patterns. Lexicalization breaks the standard PCFG assumption of independence of production, and thus, our grammar can model the more complex rhythmic dependencies which are present in musical compositions.
Using a metric we propose for the task, we show that our grammar outperforms baseline methods when run on symbolic music input which has been hand-aligned to a tatum. We also show that the grammar outperforms an existing
method when run with automatically-aligned symbolic music data as input. The code for our grammar is available at https://github.com/apmcleod/met-detection.
Using a metric we propose for the task, we show that our grammar outperforms baseline methods when run on symbolic music input which has been hand-aligned to a tatum. We also show that the grammar outperforms an existing
method when run with automatically-aligned symbolic music data as input. The code for our grammar is available at https://github.com/apmcleod/met-detection.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 14th Sound and Music Computing Conference |
| Publisher | Aalto University |
| Pages | 373-379 |
| Number of pages | 7 |
| ISBN (Print) | 978-952-60-3729-5 |
| Publication status | Published - 8 Jul 2017 |
| Event | 14th Sound and Music Computing Conference - Espoo, Finland Duration: 5 Jul 2017 → 8 Jul 2017 http://smc2017.aalto.fi/ |
Publication series
| Name | Proceedings of the 14th Sound and Music Computing Conference 2017 |
|---|---|
| Publisher | Aalto University |
| ISSN (Electronic) | 2518-3672 |
Conference
| Conference | 14th Sound and Music Computing Conference |
|---|---|
| Abbreviated title | SMC 2017 |
| Country/Territory | Finland |
| City | Espoo |
| Period | 5/07/17 → 8/07/17 |
| Internet address |