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IKA CI Music Preprocessing Listening Experiment Stimuli (2022)

<p><strong>IKA CI Music Preprocessing Listening Experiment Stimuli (2022)</strong></p> <p>This dataset contains the audio stimuli that have been presented to both cochlear implant (CI) and normal hearing (NH) listeners in the listening experiments in a study named</p> <p><em>&ldquo;A Subjective Evaluation of Different Music Preprocessing Approaches in CI Listeners&rdquo;</em></p> <p>It comprises the excerpts from the <strong>IKA CI Pop Music Dataset (IKA-CI-PMD)</strong> (<a href="https://doi.org/10.5281/zenodo.7060282">10.5281/zenodo.7060282</a>) both as unprocessed references and as processed versions where different music preprocessing strategies have been<br> applied.</p> <p>The <strong>IKA CI Pop Music Dataset</strong> is a dataset of music excerpts that has especially compiled to evaluate different music signal preprocessing strategies for CI listeners. The excerpts taken are from the <strong>MedleyDB </strong>multitrack dataset (<a href="https://medleydb.weebly.com/">https://medleydb.weebly.com/</a>) curated by <a href="https://steinhardt.nyu.edu/marl/research/resources/medleydb">Rachel Bittner et. al.</a>.</p> <p>This dataset is split into a 3-piece &ldquo;training&rdquo; set (excerpts T01 to T03) that has been used to familiarize the listeners with the experimental setup, and a 12-piece &ldquo;test&rdquo; set (E01 to E12) used in actual experiments.</p> <p>The following music preprocessing strategies are included:</p> <ul> <li>HPCA+P: Harmonic/percussive sound separation (HPSS) combined with PCA-based spectral complexity reduction [1]</li> <li>Cspl and Dspl: DNN-based remix of the harmonic and percussive portions of 4 source stems [2]</li> <li>HALCA: Remix based on a probabilistic model for melody extraction using shift invariant kernels in CQT domain [3] (accompaniment attenuated by 12 dB)</li> <li>MT remix: Oracle remixes of multitrack stems (other accompaniment attenuated by 12 dB)</li> </ul> <p>All stimuli are normalized to a loudness level of -27 LUFS. The signals are stored in the lossless FLAC format. The files contain stereo signals, where both channels are identical.</p> <p>The dataset has been compiled at the Ruhr University Bochum <a href="https://www.ruhr-uni-bochum.de/ika/index_en.html">Institute of Communication Acoustics</a> in 2022 by Johannes Gauer (johannes.gauer@rub.de) in collaboration with the fellow researchers Anil Nagathil, Benjamin Lentz, and Rainer Martin. Like MedleyDB and the IKA CI Pop Music Dataset, it is licensed under a <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.</p> <p>For further details on the included excerpts refer to the IKA CI Pop Music Dataset (IKA-CI-PMD) (<a href="https://doi.org/10.5281/zenodo.7060282">10.5281/zenodo.7060282</a>).</p> <p>[1] B. Lentz, A. Nagathil, J. Gauer, and R. Martin, &ldquo;Harmonic/Percussive sound separation and spectral complexity reduction of music signals for cochlear implant listeners,&rdquo; in Proc IEEE Int Conf Acoust Speech Signal Process ICASSP, Barcelona, Spain, May 2020, pp.&nbsp;8713&ndash;8717.</p> <p>[2] J. Gauer, A. Nagathil, K. Eckel, D. Belomestny, and R. Martin, &ldquo;A versatile deep-neural-network-based music preprocessing and remixing scheme for cochlear implant listeners,&rdquo; J. Acoust. Soc. Am., vol.&nbsp;151, no. 5, pp.&nbsp;2975&ndash;2986, May 2022.</p> <p>[3] B. Fuentes, R. Badeau, and G. Richard, &ldquo;Harmonic Adaptive Latent Component Analysis of Audio and Application to Music Transcription,&rdquo; IEEE Trans. Audio Speech Lang. Process., vol.&nbsp;21, no. 9, pp.&nbsp;1854&ndash;1866, Sep.&nbsp;2013.</p>