Source Separation of Piano Concertos Using Musically Motivated Augmentation Techniques

This is the accompanying website for the submission Source Separation of Piano Concertos Using Musically Motivated Augmentation Techniques.

  1. Yigitcan Özer and Meinard Müller
    Source Separation of Piano Concertos Using Musically-Motivated Augmentation Techniques
    IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP), 32: 1214–1225, 2024. PDF Demo DOI
    @article{OezerM24_PCSeparation_TASLP,
    author    = {Yigitcan {\"O}zer and Meinard M{\"u}ller},
    title     = {Source Separation of Piano Concertos Using Musically-Motivated Augmentation Techniques},
    journal   = {{IEEE/ACM} Transactions on Audio, Speech, and Language Processing ({TASLP})},
    year      = {2024},
    volume    = {32},
    pages     = {1214--1225},
    doi       = {10.1109/TASLP.2024.3356980},
    url-demo  = {https://www.audiolabs-erlangen.de/resources/MIR/2024-TASLP-PianoConcertoSeparation},
    url-pdf   = {2024_OezerM_PCSeparation_TASLP_ePrint.pdf}
    }

Abstract

In this work, we address the novel and rarely considered source separation task of decomposing piano concerto recordings into separate piano and orchestral tracks. Being a genre written for a pianist typically accompanied by an ensemble or orchestra, piano concertos often involve an intricate interplay of the piano and the entire orchestra, leading to high spectro–temporal correlations between the constituent instruments. Moreover, in the case of piano concertos, the lack of multi-track data for training constitutes another challenge in view of data-driven source separation approaches. As a basis for our work, we adapt existing deep learning (DL) techniques, mainly used for the separation of popular music recordings. In particular, we investigate spectrogram- and waveform-based approaches as well as hybrid models operating in both spectrogram and waveform domains. As a main contribution, we introduce a musically motivated data augmentation approach for training based on artificially generated samples. Furthermore, we systematically investigate the effects of various augmentation techniques for DL-based models. For our experiments, we use a recently published, open-source dataset of multi-track piano concerto recordings. Our main findings demonstrate that the best source separation performance is achieved by a hybrid model when combining all augmentation techniques.

Test Dataset

For assessing the quantitative and subjective evaluation of our experiments, we use the dry recordings without artificial reverberation from the Piano Concerto Dataset (PCD) as our test dataset, which contains 81 excerpts with separate piano and orchestral tracks, performed by five pianists.

Source Code

For the reproducibility of the results, we provide the open-source code and pretrained models in our GitHub repository.

Audio Examples

Excerpts selected from our test corpora, separated with different source separation models:
[Bach] [Beet1] [Beet3] [Chopin] [Grieg] [Mendel]
[Moz] [Rach2-1] [Rach2-3] [Saint] [Schum] [Tchai]

Listening Test Results

listening_test
Results of our listening tests based on the MUSHRA framework for the separated (a) piano and (b) orchestral tracks. The colored markers indicate the average rating scores enclosed by 95% confidence intervals (shown as the vertical lines).

References

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    Source Separation of Piano Concertos with Test-Time Adaptation
    In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR): 493–500, 2022. Demo
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    title     = {Source Separation of Piano Concertos with Test-Time Adaptation},
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    }
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    @article{OezerSALSM23_PCD_TISMIR,
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    year      = {2023},
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Acknowledgments

This work was supported by the German Research Foundation (DFG MU 2686/10-2). The authors are with the International Audio Laboratories Erlangen, a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institute for Integrated Circuits IIS.