Constraint-Based Time-Scale Modification of Music Recordings for Noise Beautification

This is the accompanying website for the paper Constraint-Based Time-Scale Modification of Music Recordings for Noise Beautification by Meinard Müller, Helmut Hedwig, Frank Zalkow, and Stefan Popescu.

  1. Meinard Müller, Helmut Hedwig, Frank Zalkow, and Stefan Popescu
    Constraint-Based Time-Scale Modification of Music Recordings for Noise Beautification
    Applied Sciences, 8(3), 2018. PDF Demo DOI
    @article{MuellerHZP18_NoiseBeauty_AppliedSciences,
    author  = {Meinard M{\"u}ller and Helmut Hedwig and Frank Zalkow and Stefan Popescu},
    journal = {Applied Sciences},
    title   = {Constraint-Based Time-Scale Modification of Music Recordings for Noise Beautification},
    year    = {2018},
    month   = {March},
    volume  = {8},
    number  = {3},
    articlenumber = {436},
    url-pdf  = {2018_MuellerHZP_NoiseBeauty_AppliedSciences_PrintedVersion.pdf},
    url      = {http://www.mdpi.com/2076-3417/8/3/436},
    url-demo = {https://www.audiolabs-erlangen.de/resources/MIR/2018-MRI-NoiseBeauty},
    doi      = {10.3390/app8030436},
    ISSN     = {2076-3417}
    }
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Abstract

Teaser_NoiseBeauty

In magnetic resonance imaging (MRI), a patient is exposed to beat-like knocking sounds, often interrupted by periods of silence, which are caused by pulsing currents of the MRI scanner. In order to increase the patient's comfort, one strategy is to play back ambient music to induce positive emotions and to reduce stress during the MRI scanning process. To create an overall acceptable acoustic environment, one idea is to adapt the music to the locally periodic acoustic MRI noise. Motivated by this scenario, we consider in this paper the general problem of adapting a given music recording to fulfill certain temporal constraints. More concretely, the constraints are given by a reference time axis with specified time points (e.g., the time positions of the MRI scanner's knocking sounds). Then, the goal is to temporally modify a suitable music recording such that its beat positions align with the specified time points. As one technical contribution, we model this alignment task as an optimization problem with the objective to fulfill the constraints while avoiding strong local distortions in the music. Furthermore, we introduce an efficient algorithm based on dynamic programming for solving this task. Based on the computed alignment, we use existing time-scale modification procedures for locally adapting the music recording. To illustrate the outcome of our procedure, we discuss representative synthetic and real-world examples, which can be accessed via an interactive website. In particular, these examples indicate the potential of automated methods for noise beautification within the MRI application scenario.

Examples

MRI Noise

Everyday Noise

Synthetic Noise

Sources

MRI Noise

ID Attribution Source Original license
R-MRI-06 Siemens: MRI Ex. 06
R-MRI-12 Siemens: MRI Ex. 12
R-MRI-20 Siemens: MRI Ex. 20
R-MRI-24 Siemens: MRI Ex. 24

Everyday Noise

ID Attribution Source Original license
R-RW-Train Daniel Simion: Steam Train Sound
R-RW-Snore ermine: Snoring Sound
R-RW-Heart HerbertBoland: Heartbeat Sound
R-RW-Water lharman94: Tap Dripping Sound

Synthetic Noise

ID Attribution Source Original license
R-Syn-Co Clicks with Constant Tempo
R-Syn-CoGa Clicks with Constant Tempo, and Gaps
R-Syn-CoIrGa Clicks with Constant Tempo, Gaps, and Irregularities
R-Syn-In Clicks with Increasing Tempo
R-Syn-InGa Clicks with Increasing Tempo, and Gaps
R-Syn-InIrGa Clicks with Increasing Tempo, Gaps, and Irregularities
R-Syn-Ra Random Clicks
R-Syn-RaGa Random Clicks, with Gaps

Music

ID Attribution Source Original license
S-Bach J. S. Bach: Prelude in C Major BWV 846, Piano: Kevin MacLeod
S-Fandango The Freak Fandango Orchestra: Requiem for a Fish
S-Silence Silence Is Sexy: Holiday (instrumental)
S-Broke Broke For Free: Something Elated

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