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ShareScore release 0.9.0
Dataset results
2 results for “audio effects modeling”
GCN-TFiLM: Modelling Black-box Audio Effects with Time-varying Feature Modulation
<p>Dataset, checkpoints, evaluation, and supplementary material associated with the following publication:</p> <p><a href="https://arxiv.org/abs/2211.00497">Modelling Black-box Audio Effects with Time-varying Feature Modulation</a></p> <p><strong>Citation:</strong></p> <pre><code>@inproceedings{comunita2023modelling, title={Modelling black-box audio effects with time-varying feature modulation}, author={Comunit{\`a}, Marco and Steinmetz, Christian J and Phan, Huy and Reiss, Joshua D}, booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={1--5}, year={2023}, organization={IEEE} }</code></pre> <p> </p>
Deep Learning for Black-Box Modeling of Audio Effects
<p>Accompanying audio samples for the paper:</p> <p>Martínez Ramírez M. A., Benetos, E. and Reiss J. D., “Deep Learning for Black-Box Modeling of Audio Effects” submitted to the Applied Sciences: Acoustics and Vibration - Digital Audio Effects special issue, December, 2019.</p> <p>Dry and wet bass and guitar recordings.</p> <p>Dry notes are taken from the IDMT-SMT-Audio-Effects dataset. Author: Michael Stein (Fraunhofer IDMT) https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html</p> <p>Wet notes are recorded from the limiter and preamplifier of a Universal Audio 6176 Vintage Channel Strip and from the horn and woofer of a 145 Leslie speaker cabinet.</p>
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