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Estimation of Music Recording Quality to Predict Automatic Music Transcription Performance

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Smart Multimedia (ICSM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13497))

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Abstract

Music signals can nowadays be recorded and further processed by lots of different devices in order to extract additional information like instruments and genre or use parts of those signals in various applications. Thereby, music recording quality has a big impact on all kinds of Music Information Retrieval (MIR) signal processing and their results. In this work, the recording quality of piano music is estimated by three separate neural network approaches for background noise, sound disturbances, and reverberation. The approaches for background noise and sound disturbances estimate the resulting Signal to Noise Ratio (SNR) of the music piece, the first for constant SNR and the latter for the time-dependent case. Reverberation is estimated by means of the two room parameters reverberation time and early decay time. Exemplarily, the SNR estimation results are validated in the field of piano music transcription, where the impact of the estimated recording quality on the automatic transcription results is analysed. According to those results, the piano music transcription performance can be predicted by means of the recording quality parameters.

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Notes

  1. 1.

    https://piano2notes.com.

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Authors and Affiliations

  1. Karlsruhe Institute of Technology, Institute of Industrial Information Technology, Hertzstraße 16, 76187, Karlsruhe, Germany

    Markus Schwabe, Thorsten Hoffmann & Michael Heizmann

  2. Klangio GmbH, Alter Schlachthof 39, Karlsruhe, Germany

    Sebastian Murgul

Authors
  1. Markus Schwabe
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  2. Thorsten Hoffmann
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  3. Sebastian Murgul
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  4. Michael Heizmann
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Correspondence to Markus Schwabe .

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Editors and Affiliations

  1. University of Florence, Florence, Italy

    Stefano Berretti

  2. Dolby Labs, California, CA, USA

    Guan-Ming Su

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Schwabe, M., Hoffmann, T., Murgul, S., Heizmann, M. (2022). Estimation of Music Recording Quality to Predict Automatic Music Transcription Performance. In: Berretti, S., Su, GM. (eds) Smart Multimedia. ICSM 2022. Lecture Notes in Computer Science, vol 13497. Springer, Cham. https://doi.org/10.1007/978-3-031-22061-6_24

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  • DOI: https://doi.org/10.1007/978-3-031-22061-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22060-9

  • Online ISBN: 978-3-031-22061-6

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