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Title: 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}
}

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