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Title: Piano Transcription with Convolutional Sparse Lateral Inhibition

This paper extends our prior work on contextdependent piano transcription to estimate the length of the notes in addition to their pitch and onset. This approach employs convolutional sparse coding along with lateral inhibition constraints to approximate a musical signal as the sum of piano note waveforms (dictionary elements) convolved with their temporal activations. The waveforms are pre-recorded for the specific piano to be transcribed in the specific environment. A dictionary containing multiple waveforms per pitch is generated by truncating a long waveform for each pitch to different lengths. During transcription, the dictionary elements are fixed and their temporal activations are estimated and post-processed to obtain the pitch, onset and note length estimation. A sparsity penalty promotes globally sparse activations of the dictionary elements, and a lateral inhibition term penalizes concurrent activations of different waveforms corresponding to the same pitch within a temporal neighborhood, to achieve note length estimation. Experiments on the MAPS dataset show that the proposed approach significantly outperforms a state-of-the-art music transcription method trained in the same context-dependent setting in transcription accuracy.
Authors:
 [1] ;  [1] ; ORCiD logo [2]
  1. Univ. of Rochester, NY (United States). Dept. of Electrical and Computer Engineering
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-16-28544
Journal ID: ISSN 1070-9908; TRN: US1702199
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
IEEE Signal Processing Letters
Additional Journal Information:
Journal Volume: 24; Journal Issue: 4; Journal ID: ISSN 1070-9908
Publisher:
IEEE Signal Processing Society
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Computer Science; Information Science; Mathematics; Automatic Music Transcription; Convolutional Sparse Coding; Lateral Inhibition; Offset Detection
OSTI Identifier:
1369189