2,053 research outputs found
ACCOUNTING FOR PHASE CANCELLATIONS IN NON-NEGATIVE MATRIX FACTORIZATION USING WEIGHTED DISTANCES
(c)2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Published in: Proc IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), Florence, Italy, 5-9 May 2014
Score-Informed Source Separation for Musical Audio Recordings [An overview]
(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works
Robust Joint Alignment of Multiple Versions of a Piece of Music
Large music content libraries often comprise multiple versions of a piece of
music. To establish a link between different versions, automatic music
alignment methods map each position in one version to a corresponding position
in another version. Due to the leeway in interpreting a piece, any two versions
can differ significantly, for example, in terms of local tempo, articulation,
or playing style. For a given pair of versions, these differences can be
significant such that even state-of-the-art methods fail to identify a correct
alignment. In this paper, we present a novel method that increases the
robustness for difficult to align cases. Instead of aligning only pairs of
versions as done in previous methods, our method aligns multiple versions in a
joint manner. This way, the alignment can be computed by comparing each version
not only with one but with several versions, which stabilizes the comparison
and leads to an increase in alignment robustness. Using recordings from the
Mazurka Project, the alignment error for our proposed method was 14% lower on
average compared to a state-of-the-art method, with significantly less outliers
(standard deviation 53% lower).Comment: International Society for Music Information Retrieval Conference
(ISMIR
Identifying Missing and Extra Notes in Piano Recordings Using Score-Informed Dictionary Learning
The Audio Degradation Toolbox and its Application to Robustness Evaluation
We introduce the Audio Degradation Toolbox (ADT) for the controlled degradation of audio signals, and propose its usage as a means of evaluating and comparing the robustness of audio processing algorithms. Music recordings encountered in practical applications are subject to varied, sometimes unpredictable degradation. For example, audio is degraded by low-quality microphones, noisy recording environments, MP3 compression, dynamic compression in broadcasting or vinyl decay. In spite of this, no standard software for the degradation of audio exists, and music processing methods are usually evaluated against clean data. The ADT fills this gap by providing Matlab scripts that emulate a wide range of degradation types. We describe 14 degradation units, and how they can be chained to create more complex, `real-world' degradations. The ADT also provides functionality to adjust existing ground-truth, correcting for temporal distortions introduced by degradation. Using four different music informatics tasks, we show that performance strongly depends on the combination of method and degradation applied. We demonstrate that specific degradations can reduce or even reverse the performance difference between two competing methods. ADT source code, sounds, impulse responses and definitions are freely available for download
STRUCTURED DROPOUT FOR WEAK LABEL AND MULTI-INSTANCE LEARNING AND ITS APPLICATION TO SCORE-INFORMED SOURCE SEPARATION
Notentext-Informierte Quellentrennung fĂĽr Musiksignale
codedemo: http://www.audiolabs-erlangen.de/resources/2013-ACMMM-AudioDecomp/codedemo: http://www.audiolabs-erlangen.de/resources/2013-ACMMM-AudioDecomp/codedemo: http://www.audiolabs-erlangen.de/resources/2013-ACMMM-AudioDecomp/codedemo: http://www.audiolabs-erlangen.de/resources/2013-ACMMM-AudioDecomp
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