Context-dependent piano music transcription with convolutional sparse coding
Abstract
The present disclosure presents a novel approach to automatic transcription of piano music in a context-dependent setting. Embodiments described herein may employ an efficient algorithm for convolutional sparse coding to approximate a music waveform as a summation of piano note waveforms convolved with associated temporal activations. The piano note waveforms may be pre-recorded for a particular piano that is to be transcribed and may optionally be pre-recorded in the specific environment where the piano performance is to be performed. During transcription, the note waveforms may be fixed and associated temporal activations may be estimated and post-processed to obtain the pitch and onset transcription. Experiments have shown that embodiments of the disclosure significantly outperform state-of-the-art music transcription methods trained in the same context-dependent setting, in both transcription accuracy and time precision, in various scenarios including synthetic, anechoic, noisy, and reverberant environments.
- Inventors:
- Issue Date:
- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1532026
- Patent Number(s):
- 9779706
- Application Number:
- 15/046,724
- Assignee:
- University of Rochester (Rochester, NY); Los Alamos National Security, LLC (Los Alamos, NM)
- Patent Classifications (CPCs):
-
G - PHYSICS G10 - MUSICAL INSTRUMENTS G10H - ELECTROPHONIC MUSICAL INSTRUMENTS
G - PHYSICS G10 - MUSICAL INSTRUMENTS G10G - AIDS FOR MUSIC
- DOE Contract Number:
- AC52-06NA25396
- Resource Type:
- Patent
- Resource Relation:
- Patent File Date: 2016-02-18
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; 97 MATHEMATICS AND COMPUTING
Citation Formats
Cogliati, Andrea, Duan, Zhiyao, and Wohlberg, Brendt Egon. Context-dependent piano music transcription with convolutional sparse coding. United States: N. p., 2017.
Web.
Cogliati, Andrea, Duan, Zhiyao, & Wohlberg, Brendt Egon. Context-dependent piano music transcription with convolutional sparse coding. United States.
Cogliati, Andrea, Duan, Zhiyao, and Wohlberg, Brendt Egon. Tue .
"Context-dependent piano music transcription with convolutional sparse coding". United States. https://www.osti.gov/servlets/purl/1532026.
@article{osti_1532026,
title = {Context-dependent piano music transcription with convolutional sparse coding},
author = {Cogliati, Andrea and Duan, Zhiyao and Wohlberg, Brendt Egon},
abstractNote = {The present disclosure presents a novel approach to automatic transcription of piano music in a context-dependent setting. Embodiments described herein may employ an efficient algorithm for convolutional sparse coding to approximate a music waveform as a summation of piano note waveforms convolved with associated temporal activations. The piano note waveforms may be pre-recorded for a particular piano that is to be transcribed and may optionally be pre-recorded in the specific environment where the piano performance is to be performed. During transcription, the note waveforms may be fixed and associated temporal activations may be estimated and post-processed to obtain the pitch and onset transcription. Experiments have shown that embodiments of the disclosure significantly outperform state-of-the-art music transcription methods trained in the same context-dependent setting, in both transcription accuracy and time precision, in various scenarios including synthetic, anechoic, noisy, and reverberant environments.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2017},
month = {10}
}
Works referenced in this record:
Systems and Methods for Capturing and Interpreting Audio
patent-application, March 2016
- Esparza, Tlacaelel Miguel
- US Patent Application 14/866169; 20160093278