# Constraint-Based Time-Scale Modification of Music Recordings for Noise Beautification
## 6 Experiments  
To illustrate the outcome of our noise beautification approach, we now report on experiments based on reference and source signals of various types and complexity. To make the results audible and comprehensible for the reader, the original signals as well as the experimental results have been made publicly available on the website: [[https://www.audiolabs-erlangen.de/resources/MIR/2018-MRI-NoiseBeauty](https://www.audiolabs-erlangen.de/resources/MIR/2018-MRI-NoiseBeauty)]([https://www.audiolabs-erlangen.de/resources/MIR/2018-MRI-NoiseBeauty](https://www.audiolabs-erlangen.de/resources/MIR/2018-MRI-NoiseBeauty)), under a suitable Creative Commons license. The reference signals include recordings of four representative MRI noise signals, everyday noise, as well as synthetically generated pulse click tracks. An overview of these reference signals along with a short description and identifiers can be found in Table 1. For example, the signal R-MRI-06 is an MRI signal that consists of periodically spaced knocking sounds interleaved with periods of silence (see Figure 6a). As for the source signals, we used four music signals of different genre. Table 2 yields a description of these music recordings. In a preprocessing step, we determined suitable APs for all reference and source signals. To this end, we applied automated onset and pulse tracking procedures as described in [27] (Chapter 6). As for the reference signals, a simple onset detector (see, e.g., [28]) was sufficient to determine the positions of the transient-like knocking or clicking noise sounds. These positions define the reference APs. As for the source signals, we first specified the rough tempo that corresponds to the metrical level (e.g., sixteenth, eighth, quarter, or half note level) to be considered. Figure 4 illustrates this by means of the source signal S-Bach. Then, we applied the predominant pulse tracking procedure from [29] to determine the positions of the pulses on that level. These positions define the source APs. In our experiments, we used implementations provided by the publicly available tempogram toolbox ([[https://www.audiolabs-erlangen.de/resources/MIR/tempogramtoolbox](https://www.audiolabs-erlangen.de/resources/MIR/tempogramtoolbox)]([https://www.audiolabs-erlangen.de/resources/MIR/tempogramtoolbox](https://www.audiolabs-erlangen.de/resources/MIR/tempogramtoolbox))) (see [30]) as well as a beat tracker by [PERSON] ([[https://labrosa.ee.columbia.edu/projects/coversongs](https://labrosa.ee.columbia.edu/projects/coversongs)]([https://labrosa.ee.columbia.edu/projects/coversongs](https://labrosa.ee.columbia.edu/projects/coversongs))) (see [31]). Figure 3: Computation and application of a time-stretch function \(\tau\). (**a**) Definition of anchor points based on a valid mapping function \(\varphi\); (**b**) Definition of time-stretch function \(\tau\) based on anchor points; (**c**) Time axis with source APs after applying the time-stretch function \(\tau\). Based on the reference APs of a noise signal and the source APs of a music signal, we then applied the DP-based algorithm (see Section 4.1) as well as the greedy algorithm (see Section 4.2) to determine a mapping function. Subsequently, we utilized the procedure from Section 5 to temporally adapt the source signal to run synchronously to the reference signal. Finally, we generated an audio file, where we superimposed the original reference signal with the modified source signal. This audio file represents our final output. To evaluate the result, we also visualized the positions of the reference APs and of the modified source APs as in Figure 1d. The visualization along with the audio file was then integrated into an interactive website using the trackswitch.js framework [32] ([[https://audiolabs.github.io/trackswitch.js](https://audiolabs.github.io/trackswitch.js)]([https://audiolabs.github.io/trackswitch.js](https://audiolabs.github.io/trackswitch.js))).