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8 results for “source separation, music”
Stimuli for the paper Perceptual Evaluation of Source Separation for Remixing Music
<p>Stimuli for the paper</p> <p>H. Wierstorf, D. Ward, R. Mason, E. M. Grais, C. Hummersone, M. D. Plumbley, "Perceptual Evaluation of Source Separation for Remixing Music," in 143rd Convention of the Audio Engineering Society, 2017.</p> <p>The files used for the experimental procedure are available at https://doi.org/10.5281/zenodo.835191</p> <p>The stimuli in this publication are based on the DSD100 and submission files of the SiSEC challenge, see https://www.sisec17.audiolabs-erlangen.de</p>
Blind Source Separation of Musical Notes
<p>BLIND SOURCE SEPARATION OF MUSICAL NOTES.</p> <p>This dataset contains 12 groups of three musical notes whose pitches are not in harmonic relation. They were extracted from the RWC Musical Instrument Sound Database [1] and were used for performance evaluation of an audio source separation system, proposed in my PhD thesis [2], in Chapter 5, Section 5.8.2, on page 150.</p> <p>[1] M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, "RWC music database: music genre database and musical instrument sound database," in Proceedings of the 4th International Conference on Music Information Retrieval, pp. 229-230, 2003.</p> <p>[2] Delgado Castro, A. "Iterative separation of note events from single-channel polyphonic recordings". Ph.D. University of York. 2019.</p>
SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation
<p>The SynthSOD dataset contains more than 47 hours of multitrack music obtained by synthesizing orchestra and ensemble pieces from the <a href="https://qsdfo.github.io/LOP/database.html" target="_blank" rel="noopener">Symbolic Orchestral Database (SOD)</a> using Spitfire BBC Symphony Orchestra Professional Library. To synthesize the MIDI files from the SOD, we needed to fix the original files into the General MIDI standard, select a subsect of files that fitted into our requirements (e.g., containing only instruments that we could synthesize), and develop a new system to generate musically-motivated random annotations about tempo, dynamic, and articulation. The code to replicate this process is available in <a href="https://github.com/repertorium/HQ-SOD-generator" target="_blank" rel="noopener">our repository</a> and all the details can be read in <a href="https://doi.org/10.1109/OJSP.2025.3528361" target="_blank" rel="noopener">our paper</a>. We have also published the code to train and evaluate the baseline and the pre-trained models in a <a href="https://github.com/repertorium/SynthSOD-Baseline" target="_blank" rel="noopener">GitHub repository</a>.</p> <p>We have also published the aligned score information for most of the pieces <a href="https://doi.org/10.5281/zenodo.14971533">here</a>.</p>
HHDS - Spanish HipHop Dataset for Music Source Separation
<p><strong>What is HHDS?</strong></p> <p>HHDS is a reduced compilation of Hip Hop songs, used to train a Convolutional Neural Network (CNN) for audio source separation in [1], built on top of the DeepConvSep framework [2] developed at the Music Technology Group (MTG), Universitat Pompeu Fabra.</p> <p>The structure of HHDS follows the convention of DSD100 [3] (Demixing Secrets Dataset). HHDS contains the separated tracks for the categories of bass, drums, vocals and others in monophonic WAV les with a sampling rate of 44100Hz. The mixture is calculated by normalizing the sum of the tracks. The main difference with respect to DSD100 is that in HHDS there are HipHop songs only, instead of many different genres. The total number of songs is 18, from which 13 are used for training and 5 are used for evaluation.</p> <p>A detailed list of the songs included in the dataset can be found inside the .zip file provided. The reader can also find the code for this dataset in the DeepConvSep repository in the path examples/hiphopss.</p> <p> </p> <p><strong>References</strong></p> <p>[1] "A Deep Learning Approach to Source Separation and Remixing of HipHop music", Héctor Martel, Undergraduate Thesis, Universitat Pompeu Fabra 2016-2017. </p> <p>[2] DeepConvSep repository on GitHub: https://github.com/MTG/DeepConvSep</p> <p>[3] Demixing Secrets Dataset (DSD100), SiSEC2016: http://liutkus.net/DSD100.zip</p>
Experimental procedure for the paper Perceptual Evaluation of Source Separation for Remixing Music
<p>All the files you need to rerun the experiment described in the paper:</p> <p>H. Wierstorf, D. Ward, R. Mason, E. M. Grais, C. Hummersone, M. D. Plumbley, "Perceptual Evaluation of Source Separation for Remixing Music," in 143rd Convention of the Audio Engineering Society, 2017.</p> <p>The actual stimuli are not part of this publication, but can be regenerated or be downloaded from https://doi.org/10.5281/zenodo.835182<br> </p>
Figures and data for the paper Perceptual Evaluation of Source Separation for Remixing Music
<p>Listening test results and figures for the paper:</p> <p>H. Wierstorf, D. Ward, R. Mason, E. M. Grais, C. Hummersone, M. D. Plumbley,<br> "Perceptual Evaluation of Source Separation for Remixing Music," in 143rd<br> Convention of the Audio Engineering Society, 2017.</p> <p>`fig02/data/` contains the results from single listeners and median results across<br> listeners.<br> `fig02/fig02.plt` is the code to regenerate `fig02/fig02.pdf`.</p> <p>`fig03/data/` contains average medians across all songs.<br> `fig03/fig03.plt` is the code to regenerate `fig03/fig03.pdf`.</p> <p>The listening test results were extracted from the raw data stored together with<br> the experimental procedure in https://doi.org/10.5281/zenodo.835191.<br> There the file `experiment/saves/ratings/analyze_results.py` can be run in order<br> to regenerate the result files provided with this publication.<br> </p>
CrossNet-Open-Unmix for Music Source Separation (X-UMXL)
<p>Weights of CrossNet-Open-Unmix (X-UMX) trained on the internal 100h dataset which is larger than <a href="https://sigsep.github.io/datasets/musdb.html">MUSDB18</a>, named X-UMX Large (X-UMXL). The weights can be used with <a href="https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX">X-UMX on Asteroid (PyTorch)</a>. The details of X-UMX are described in <a href="https://ieeexplore.ieee.org/document/9414044">here</a>.</p>
Support Material for: S.I. Mimilakis et al., ``Examining the Perceptual Effect of Alternative Objective Functions for Deep Learning Based Music Source Separation''
<p>The audio corpus used for conducting the listening tests reported in S.I. Mimilakis et al., ``Examining the Perceptual Effect of Alternative Objective Functions for Deep Learning Based Music Source Separation''.</p>
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