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3 results for “Mono recordings”
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Discrete)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Mono Continuous)
<p><strong>GUITAR-FX-DIST</strong> is a dataset of electric guitar recordings processed with overdrive, distortion and fuzz audio effects. It was developed for research in guitar effects detection, classification and parameters estimation. The dataset is also useful for research on automatic music transcription, intelligent music production, signal processing or effects modelling. It contains both unprocessed and processed recordings.</p> <p>The dataset is split into 4 sub-datasets: Mono Continuous, Mono Discrete, Poly Continuous, Poly Discrete</p> <p> </p> <p><strong>Authors:</strong></p> <p>Marco Comunità - <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London</p> <p> </p> <p><strong>Reference:</strong></p> <p>If you make use of GUITAR-FX-DIST, please cite the following publication:</p> <pre><code>@article{comunità2021guitar, title={Guitar Effects Recognition and Parameter Estimation with Convolutional Neural Networks}, author={Comunità, Marco and Stowell, Dan and Reiss, Joshua D.}, journal={Journal of the Audio Engineering Society}, year={2021}, volume={69}, number={7/8}, pages={594-604}, doi={}, month={July} }</code></pre> <p> </p> <p><strong>Dataset Snapshot:</strong></p> <ul> <li><strong>Size:</strong> ~550k samples (~305 hours) + 550k mel spectrograms</li> <li><strong>Audio Format:</strong> WAV - 44.1kHz, 16bit, mono, -6dBFS</li> <li><strong>Mel-Spectrogram Format:</strong> NPY - 128 frequency bands, sample rate 22050Hz, window length 1024, hop size 512,</li> <li><strong>Effects:</strong> 14 between overdrive, distortion and fuzz</li> <li><strong>Unprocessed recordings</strong> <ul> <li>624 monophonic notes</li> <li>420 polyphonic (2, 3 and 4 notes intervals and chords)</li> <li>2 guitars, with up to 2 pick-up settings and up to 3 plucking styles (finger pluck - hard, finger pluck - soft, pick) <ul> <li>Schecter Diamond C-1 Classic</li> <li>Chester Stratocaster</li> </ul> </li> </ul> </li> <li><strong>Samples length:</strong> 2 sec</li> </ul> <p> </p> <p><strong>Unprocessed Recordings:</strong></p> <p>The original (unprocessed) recordings are from the <a href="https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html">IDMT-SMT-Audio-Effects</a> dataset.</p> <p>For details please refer to the website and the accompagning publication:</p> <p><em>Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald: Automatic Detection of Audio Effects in Guitar and Bass Recordings. Proceedings of the AES 128th Convention, 2010.</em></p> <p> </p> <p><strong>Processed Recordings:</strong></p> <p>The processed recordings are divided into 4 sub-datasets which are named depending on the unprocessed recordings used (monophonic or polyphonic) and on the settings' values (discrete or continuous).</p> <p>The sub-datasets are called: Mono Discrete, Poly Discrete, Mono Continuous, Poly Continuous</p> <p>Mono Discrete and Poly Discrete use a discrete set of combinations selected as the most common and representative settings a person might use (see README file for details).</p> <p>For Mono Continuous and Poly Continuous both unprocessed samples as well as settings’ values are drawn from a uniform distribution (10000 samples for each effect).</p> <p>Samples:</p> <ul> <li>Mono Discrete: ~160k</li> <li>Poly Discrete: ~110k</li> <li>Mono Continuous: 140k</li> <li>Poly Continuous: 140k</li> </ul> <p> </p> <p><strong>Scripts:</strong></p> <p>The dataset includes the MATLAB scripts used to generate the samples</p>
Immersive Audio Remixing of Mono Recordings of the 1950s and 1960s: Rediscovering Musical Treasures from the Past
<p><strong>IMPORTANT NOTE</strong></p> <p>The song <em>B-A-B-Y</em> was written by Isaac Hayes and David Porter, then also arranged by Booker T. Jones and Steve Cropper, and performed by Carla Thomas in 1966.</p> <p>The following binaural sound remixes of <em>B-A-B-Y</em> have been performed in a research context, with four various HRTF profiles coming from the Listen HRTF Database by Ircam, for the AES research paper “Immersive Audio Remixing of Mono Recordings of the 1950s and 1960s: Rediscovering Musical Treasures from the Past”, presented at the 156th AES Convention on June 15-17, 2024, in Madrid, Spain. </p> <p>I, the undersigned Jean Viardot, the first author of this paper, testify that nothing about the composition, the arrangement, or any element of the musical content of the song, has been manipulated to obtain this remix. Only the sound rendering of a digitalized version of the original master has been manipulated and modified to address the research questions being stated in the abovementioned paper.</p> <p>To date (5/11/2024), I have not made any profit with this remix, and I forbid anyone to publish it without my permission. For any intention to publish, broadcast, or market this remix, please contact me beforehand.</p>
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