Modeling plate and spring reverberation using a DSP-informed deep neural network
<p>Accompanying audio samples for the paper:</p> <p>Martínez Ramírez M. A., Benetos, E. and Reiss J. D., “Modeling plate and spring reverberation using a DSP-informed deep neural network” in the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.</p> <p>Dry and wet bass and guitar recordings.</p> <p>Bass and Guitar 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>Plate Reverb - Bass - recordings are taken from the IDMT-SMT-Audio-Effects dataset. Plate settings are the following:</p> <ul> <li><strong>Smaertelectronix ambience</strong>: ’Gating Amount - 0’, ’Gating Attack" - 10 ms’, ’Gating Release - 10 ms’, ’Decay Time - 2225 ms’, ’Decay Diffusion - 50%’, ’Decay Hold - off’, ’Shape Size - 16%’, ’Shape Predelay - 0 ms’, ’Shape Width - 100%’, ’Shape Quality - 100%’, ’Shape Variation - 0’, ’EQ Bass Frequency - 43 Hz’, ’EQ Bass Gain - −7.8 dB’, ’EQ Treble Frequency - 5044 Hz’, ’EQ Treble Gain - −3.7 dB’, ’Damping Bass Frequency - 158 Hz’, ’Damping Bass Amount - 87%’, ’Damping Treble Frequency - 8127 Hz’, ’Damping Treble Amount - 32%’, ’Dry - −Inf’, ’Wet - 0dB’.</li> </ul> <p>Spring Reverb - Bass and Guitar - recorded from the spring reverb tank<strong>: Accutronics </strong><strong>4</strong><strong>EB</strong><strong>2</strong><strong>C</strong><strong>1</strong><strong>B</strong>: ’Dry Mix - 0%’, ’Wet Mix - 100%’</p> <p>Plate<em> </em>reverb samples correspond to a VST audio plug-in, while spring<em> </em>reverb samples are recorded using an analog reverb tank which is based on 2 springs placed in parallel.</p> <p>The recordings are downsampled to 16 kHz. Also, since the plate reverb samples have a fade-out applied in the last 0.5 seconds of the recordings, we process the spring reverb samples accordingly.</p>
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28/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 0
- Engagement
- 0