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43 results for “guitar”
Five guitar dataset
<p>This dataset contains 30 guitar performances of 6 different guitar songs (5 recordings/song, each of the 5 recordings with a different guitar) recorded simultaneously from 3 different setups (DI, mobile mic, and computer microphone). In total there are 90 recordings.</p> <p>The songs recorded are:</p> <ul> <li>Lily Was Here (Lily)</li> <li>Mountain At My Gates (Mountain)</li> <li>Where Did You Sleep Last Night (Where)</li> <li>20th Century Boy (Century)</li> <li>Runaway Train (Train)</li> <li>Hole In My Shoe (Hole)</li> </ul> <p>The guitars used are:</p> <ul> <li>Fender Player Telecaster3 (Telecaster)</li> <li>Ibanez ART 120 with EMG pickups (Ibanez)</li> <li>Epiphone Joe Pass Emperor-II PRO5 (Epiphone)</li> <li>Larrivée OM-406 (Larrivee)</li> <li>Eastman E1OM 7 (Eastman)</li> </ul> <p>The files are named according to the following pattern:</p> <p><em>SongName_GuitarUsed_Tempo_RecordingSetUp.wav</em></p> <p>How the dataset was made:</p> <p>The setup used a Yamaha Audio Gram 6 audio interface and Reaper9 to record guitar from direct input (DI), a Huawei P30 lite model MAR-LX1A to record guitar from a mobile, and a DELL Inspiron 13 5000 to record guitar from a computer. To record acoustic guitar in DI, I used a Fishman Neo D Single Coin on Larrivée and Seymour Duncan Seth love on the Eastman connected to a NAP-5 Stageman Floor acoustic preamp going then to the audio interface. The computer and mobile were positioned approximately 3.8 cm from the player position (measured from the twelve fret of the guitars). The electric guitars were plugged into a DV Mark Little Jazz guitar amp which has a DI output, that was connected to the audio interface. The amp was located at a distance of 114.5 cm from the mobile and the computer.</p>
GUITAR-FX-DIST: A Dataset of Processed Guitar Recordings for Music Research - (Poly 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>
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 - (Poly 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>
EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb
<p>EGFxSet (Electric Guitar Effects dataset) features recordings for all clean tones in a 22-fret Stratocaster, recorded with 5 different pickup configurations, also processed through 12 popular guitar effects. Our dataset was recorded in real hardware, making it relevant for music information retrieval tasks on real music. We also include annotations for parameter settings of the effects we used.</p> <p>More details can be found in <a href="http://egfxset.github.io">egfxset.github.io</a></p> <p>The dataset can also be accessed with <a href="https://mirdata.readthedocs.io/en/stable/source/mirdata.html#module-mirdata.datasets.egfxset">mirdata</a></p> <p>Effects and parameters included:</p> <table> <tbody> <tr> <td>Effect</td> <td>Model</td> <td>Effect Type</td> <td>Knob Names</td> <td>Knob Type</td> <td>Setting</td> </tr> <tr> <td>blues driver</td> <td>Boss BD-2 Blues Driver</td> <td>distortion</td> <td>['level', 'tone', 'gain']</td> <td>['volume','eq','effect amount']</td> <td>[0.5,0.5,1.0]</td> </tr> <tr> <td>tube screamer</td> <td>Ibanez Mini Tube Screamer</td> <td>distortion</td> <td>['tone', 'overdrive', 'level']</td> <td>['eq','effect amount','volume']</td> <td>[0.5,1.0,0.5]</td> </tr> <tr> <td>distortion</td> <td>Pro Co Sound RAT2 Distortion</td> <td>distortion</td> <td>['distortion', 'filter', 'volume']</td> <td>['effect amount','eq','volume']</td> <td>[1.0, 0.5,1.0]</td> </tr> <tr> <td>chorus</td> <td>Boss CE-3 Chorus</td> <td>modulation</td> <td>['rate', 'depth', 'stereo mode']</td> <td>['rate','effect amount','selector']</td> <td>['120 bpm', 1.0, False]</td> </tr> <tr> <td>flanger</td> <td>Mooer E-Lady</td> <td>modulation</td> <td>['color', 'type', 'range', 'rate']</td> <td>['eq','selector','effect amount','rate']</td> <td>[0.5, 'normal', 1.0, '120 bpm']</td> </tr> <tr> <td>phaser</td> <td>MXR Phase 45</td> <td>modulation</td> <td>['speed']</td> <td>['rate']</td> <td>['120 bpm']</td> </tr> <tr> <td>tape echo</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (bass)', 'tweez (treble)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'eq', 'eq', 'effect amount']</td> <td>['tape echo', '120 bpm', 0.6, 0.5, 0.5, 0.5]</td> </tr> <tr> <td>digital delay</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (bass)', 'tweez (treble)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'eq', 'eq', 'effect amount']</td> <td>['digital delay', '120 bpm', 0.6, 0.5, 0.5, 0.5]</td> </tr> <tr> <td>sweep echo</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (sweep speed)', 'tweez (sweep depth)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'rate', 'effect amount', 'effect amount']</td> <td>['sweep echo', '120 bpm', 0.6, '120 bpm',1.0,0.5]</td> </tr> <tr> <td>plate reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'plate', 1.0, 0.2, True]</td> </tr> <tr> <td>hall reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'hall', 1.0, 0.2, True]</td> </tr> <tr> <td>spring reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'spring', 1.0, 0.2, True]</td> </tr> </tbody> </table> <p><br>Please cite these papers if using EGFxSet:</p> <p>Pedroza HE, Abreu W, Corey R, Roman IR. "Leveraging real electric guitar tones and effects to improve robustness in guitar tablature transcription modeling." <em>In 27th International Conference on Digital Audio Effects (DAFx),</em> 2024.</p> <p>Pedroza, Hegel, Gerardo Meza, and Iran R. Roman. "EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb." <em>ISMIR Late Breaking Demo, </em>2022.</p>
Acoustic Guitar Timbre Thematic Analysis
<p>A perceptual study was conducted to investigate listener perceptions of acoustic guitar timbre, encompassing descriptive and preference analysis, and the impact of guitar playing style on perceived timbre similarity and preference.</p> <p>The listening test was based on recordings of ten different steel-string acoustic guitars at various price points, sourced from the online music retailer Thomann (https://www.thomann.de/). For each guitar, recordings of three different songs were used, each with a different playing style: picking (mainly individual notes played with a combination of fingers and pick), strumming (mainly chords played with a pick), and fingerstyle (strings plucked with fingers rather than a pick).</p> <p>The study was completed by 27 participants (8 female, 19 male, mean age: 27) of 14 different nationalities. Participants had an advanced musical proficiency (Goldsmiths Musical Sophistication Index General Sophistication score of 97.85) and 11 of them played guitar as their primary instrument. Participants listened to each guitar in each of the three playing styles and were asked to describe the instrument's timbre, what they liked and what they disliked.</p> <p>We conducted a thematic analysis of the participant answers to the three questions (timbre description, timbre like, timbre dislike) for the ten guitars using a combination of deductive and inductive approaches. This dataset provides the analysis codebook, theme and code statistics.</p> <p>Details about the thematic analysis and the study can be found in the accompanying publication currently under revision (the reference will be added in due course).</p>
A Small Dataset of Jazz Guitar Licks for Automatic Transcription Experiments
<p>A dataset consisting of ten (mostly monophonic) jazz guitar licks that were performed by François Pachet and recorded at the Sony Computer Science Laboratory (CSL) in Paris.</p>
Musical Performance Critique Documents for Guitar
<p>CROCUS (CRitique dOCUmentS): Dataset of Musical Performance Critique Documents (in Japanese) CC BY-NC-ND 4.0</p> <p>This open dataset contains 84 guitar performances and 252 critiques of those performances.</p> <p>For more information, please visit the project page below.<br><a href="https://masaki-cb.github.io/crocus/">https://masaki-cb.github.io/crocus/</a></p> <p> </p> <p>n01 F. Sor Etude, Op. 31-1<br>n02 F. Sor Etude, Op. 35-22<br>n03 M. Carcassi Etude, Op. 60-3<br>n04 Anonymous Romanza<br>n05 F. Tárrega Lagrima<br>n06 L. Walker Kleine Romanze<br>n07 J. S. Sagreras Maria Luisan</p> <p> </p>
AG-PT-set: Acoustic Guitar Playing Technique dataset
<p>This is the Acoustic Guitar Playing Technique dataset (AG-PT-set).<br>It contains <strong>15 hours and 55 minutes</strong> of monophonic recordings of <strong>12</strong> expressive guitar playing techniques (pitched and percussive). <br>Of these, <strong>10 hours and 4 minutes</strong> of recordings encompassing 8 of the 12 techniques have been labeled, meaning that onsets were identified at the millisecond level and their timestamp was saved, alongside playing technique information.<br>As a result, <strong>32,592</strong> individual notes have been labeled.<br>Recordings were performed by <strong>6 players</strong> on <strong>7</strong> different acoustic steel-string <strong>guitars</strong>.</p> <p>The techniques are the following:</p> <ol> <li><strong>Kick technique</strong> (<em>*percussive*</em>): producing a sound that resembles a kick drum by hitting the lower right part of the top of the guitar body;</li> <li><strong>Snare-A technique </strong>(<em>*percussive*</em>): producing a sound by hitting the lower right side of the guitar body;</li> <li><strong>Tom technique</strong> (<em>*percussive*</em>): producing a sound by hitting the area of the guitar body near the top of the end of the fretboard, using the thumb;</li> <li><strong>Snare-B technique</strong> (<em>*percussive*</em>): producing a sound by hitting the muted strings over the end of the fretboard;<br> </li> <li><strong>Natural Harmonics</strong> (pitched): plucking the strings while lightly touching the string with the fretting finger (i.e., not pressing the string fully), therefore letting only some harmonic overtones ring;</li> <li><strong>Palm Mute</strong>: partially muting the strings with the palm of the picking hand, resulting in a muffled sound.</li> <li><strong>Pick Near Bridge</strong> (pitched): plucking the string near the guitar bridge, producing sounds with great high-frequency content;</li> <li><strong>Pick Over the Soundhole</strong> (pitched): plucking the string over the soundhole, producing sounds with lower treble content and greater intensity;</li> <li><strong>Bending technique</strong> (pitched): pulling the strings, raising the pitch (half-tone interval);</li> <li><strong>Hammer-on technique</strong> (pitched): sharply bringing a finger down onto the fingerboard, creating a legato sound (half-tone interval);</li> <li><strong>Staccato</strong> (pitched): playing short notes;</li> <li><strong>Vibrato</strong> (pitched): Moving the fretting finger to warp the pitch and tone of the sound.</li> </ol> <p>Techniques 1 through 8 have been meticulously labeled.</p> <p> </p> <p>This dataset is presented in detail in the following conference paper:</p> <div>D. Stefani, G. A. Giudici, and L. Turchet. 2024. <strong>On the Importance of Temporally Precise Onset Annotations for Real-Time Music Information Retrieval: Findings from the AG-PT-set Dataset</strong>. In Proceedings of the 19th International Audio Mostly Conference: Explorations in Sonic Cultures (AM '24). </div> <p><a href="https://doi.org/10.1145/3678299.3678325">https://doi.org/10.1145/3678299.3678325</a></p>
1964 flamenca negra guitar from Faustino Conde - documentation and reverse engineered construction plans
<p>This data set documents a specific guitar made by Faustino Conde in Madrid in 1964.<br>(Comment: Felipe Conde identified the handwriting of Mariano Conde in the "Conde" signature on the label inside, the signature of his father. This does not necessarily mean that the guitar was built by Mariano, since experts believe, that it was the brother Faustino who built these kind of special guitars, as it has also been the case for the flamenca negra guitars).</p> <p>The guitar is a so-called flamenca negra, a flamenco guitar with rosewood used for rib and back plate.<br>(Errata: the guitar was sold as a flamenca negra, and experts in Granada also believed it is a flamenca negra. However, Felipe Conde now inspected the guitar and states that the instrument has been built as a classical guitar with only some changes in the setup that might lead to other conclusions. It is not clear whether this setup is original from Conde or whether the guitar has been changed in an aftermath.)</p> <p>The guitar has the exceptional character of showing four main resonances between the Helmholtz resonance (A0) and the first air mode (A1), while other guitars usually have one or two resonances in the same range.<br>This is observable across a wider range of old and contemporary guitars, documented in an archive.<br>Mores, R. 2021a. ‘Archive for the acoustical documentation of classical Spanish guitars, flamenco guitars and romantic guitars from private and public collections – bridge mobility’. <em>Zenodo</em>,<br><a href="https://doi.org/10.5281/zenodo.4604577" target="_blank" rel="noopener">doi: 10.5281/zenodo.4604577</a>.<br>This observation triggered a research project to understand this.</p> <p>The resulting analytical model reveals the delicate tuning of related parameters in the construction, published in the Journal of the Acoustical Society of America, JASA. <br>Mores, R. 2021. ‘Sound tuning in asymmetrically braced guitars’. <em>J. Acoust. Soc. Am.</em> 149(2), 1041–1057.<br><a href="https://doi.org/10.1121/10.0003378" target="_blank" rel="noopener">https://doi.org/10.1121/10.0003378</a>.<br>This model explains how to design multiple resonances into a guitar so that the fundamental tone is suported for every semitone played. The model matches with findings not only of this Conde guitar but explains tuning issues in general. There should be a translation into Spanish in due time.</p> <p>A brief talk (English) explains the main issues and demonstrates the congruence between the analysis and an mechanical model, build for demonstration purposes.<br>Mores, R. 2020a. ‘Tuning signature modes in guitars - a lesson by Faustino Conde’. <em>Zenodo</em>, <br><a href="https://doi.org/10.5281/zenodo.4624826" target="_blank" rel="noopener">doi: 10.5281/zenodo.4624826</a>.</p> <p>Supporting material (coded in MATLAB) allows researchers and guitar makers to explore the issue.<br>Mores, R. 2020. ‘Tuning asymmetrically braced guitars - analytical models and MATLAB code’, <em>Zenodo</em>, <br><a href="https://doi.org/10.5281/zenodo.4010596" target="_blank" rel="noopener">doi: 10.5281/zenodo.4010596</a>.</p> <p>This publication documents the construction plans of the Faustino Conde guitar.<br>Guitar makers asked for these plans to understand the principles of parameter tuning based on the construction. The documentation comprises:<br>1. construction plans (high resolution)<br>2. photos of the total instrument<br>3. photos of details inside </p> <p>***</p> <p>Comments on the guitar for those who consider to build this guitar model.<br>1. The guitar is original. Also the top and the mechanics. Experts in Granada, while inspecting the varnish, believed that the top plate might have been modified. However, Felipe Conde (*1959) states that the guitar is original. This also includes the fretboard. The fretboard height is declining towards the body and this caused questions by Granadian guitar makers. However, in the workshop of the Conde family several inspected guitars from the 50s through the 70s revealed a likewise decline of the height.<br>2. Felipe Conde inspected the guitar. He believes that the guitar is an experimental guitar and that it has been built as a customized guitar for a highly professional musician. But there are no records on this.<br>3. The setup of the guitar has been modified. The height of the bridge is lowered where the bone sits. This can be inspected in the construction plan. It is not known whether this was done by Conde himself or by someone else in an aftermath. Experts in Granada but also members of the Conde family state that the present setup is perfect.</p>
IDMT-SMT-Guitar Dataset
<p>The IDMT-SMT-GUITAR database is a large database for automatic guitar transcription. Seven different guitars in standard tuning were used with varying pick-up settings and different string measures to ensure a sufficient diversification in the field of electric and acoustic guitars. The recording setup consisted of appropriate audio interfaces, which were directly connected to the guitar output or in one case to a condenser microphone. The recordings are provided in one channel RIFF WAVE format with 44100 Hz sample rate.<br> <br> The dataset consists of four subsets. The first contains all introduced playing techniques (plucking styles: finger-style, muted, picked; expression styles: normal, bending, slide, vibrato, harmonics, dead-notes) and is provided with a bit depth of 24 Bit. It has been recorded using three different guitars and consists of about 4700 note events with monophonic and polyphonic structure. As a particularity the recorded files contain realistic guitar licks ranging from monophonic to polyphonic instrument tracks.</p> <p>The second subset of data consists of 400 monophonic and polyphonic note events each played with two different guitars. No expression styles were applied here and each note event was recorded and stored in a separate file with a bit depth of 16 Bit. The parameter annotations for the first and second subset are stored in XML format.</p> <p>The third subset is made up of five short monophonic and polyphonic guitar recordings. All five pieces have been recorded with the same instrument and no special expression styles were applied. The files are stored with a bit depth of 16 Bit and each file is accompanied by a parameter annotation in XML format.</p> <p>Additionally, a fourth subset is included, which was created for evaluation purposes in the context of chord recognition and rhythm style estimation tasks. This set contains recordings of 64 short musical pieces grouped by genre. Each piece has been recorded at two different tempi with three different guitars and is provided with a bit depth of 16 Bit. Annotations regarding onset positions, chords, rhythmic pattern length, and texture (monophony/polyphony) are included in various file formats.</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>
Sculpture Man with Guitar
[1948.12.0155](http://collections.smvk.se/carlotta-vkm/web/object/21244) Mansfigur i svart guallacan-trä, spelande på gitarr. Används av medicinmannen vid botande av sjuka. Source: Objaverse 1.0 / Sketchfab
Fingerboard Markers 5f for Guitars that are Configured with All-Fourths Tuning
<p><strong>General Description:</strong></p><p>The files that are included in this archive ("Fingerboard_Markers_5f.zip", DOI: 10.5281/zenodo.10119514) describe a novel system of fingerboard markers that is intended for 6-string guitars that are configured with the following all-fourths tuning system: </p><ul><li>String number 6 is tuned to an open pitch of E2, which has a tuning frequency of 82.407 hertz (cycles per second).</li><li>String number 5 is tuned to an open pitch of A2, which has a tuning frequency of 110.000 hertz (cycles per second).</li><li>String number 4 is tuned to an open pitch of D3, which has a tuning frequency of 146.832 hertz (cycles per second).</li><li>String number 3 is tuned to an open pitch of G3, which has a tuning frequency of 195.998 hertz (cycles per second).</li><li>String number 2 is tuned to an open pitch of C4, which has a tuning frequency of 261.626 hertz (cycles per second).</li><li>String number 1 is tuned to an open pitch of F4, which has a tuning frequency of 349.228 hertz (cycles per second).</li></ul><p> </p><p>Furthermore, the fingerboard markers that are described by the files that are included in this archive ("Fingerboard_Markers_5f.zip", DOI: 10.5281/zenodo.10119514) were designed for guitars that exhibit the following specifications: </p><ul><li>Scale Length: 647.7 mm</li><li>String Spacing at the Nut: 7.04 mm</li><li>String Spacing at the Bridge: 10.5 mm</li><li>Number of frets: 22</li></ul><p> </p><p><strong>Definitions:</strong></p><p>If the fingerboard of a guitar is viewed while the longitudinal axis of the guitar neck is oriented vertically, with the nut at the top and the bridge at the bottom, then it is assumed herein that the strings of the guitar are numbered sequentially from string number 1 to string number 6, wherein string number 1 is positioned nearest to the right edge of the fingerboard and string number 6 is positioned nearest to the left edge of the fingerboard. </p><p>The term "String Spacing" herein denotes the distance between the centroidal axes of any two adjacent strings. </p><p> </p><p><strong>Contents of this Archive:</strong></p><ul><li>"Fingerboard_Markers_5f.DXF": Full-scale drawing of the complete system of fingerboard markers.</li><li>"Fingerboard_Markers_5f.pdf": Full-scale drawing of the complete system of fingerboard markers.</li><li>"Fingerboard_Markers_5f.svg": Full-scale drawing of the complete system of fingerboard markers.</li><li>"Fingerboard_Markers_5f_LICENSE.pdf": The license that applies to the system of fingerboard markers that is described by the contents of this archive ("Fingerboard_Markers_5f.zip", DOI: 10.5281/zenodo.10119514).</li><li>"Fingerboard_Markers_5f_Notes.pdf": Schematic diagram of the notes that surround each fingerboard marker.</li><li>"Fingerboard_Markers_5f_Notes.svg": Schematic diagram of the notes that surround each fingerboard marker.</li><li>"Fingerboard_Markers_5f_ReadMe.pdf": This document.</li></ul><p> </p><p><strong>Copyright and License:</strong></p><p>Copyright © 2023 Hart Honickman</p><p>Copyright in the system of fingerboard markers that is described by the contents of this archive ("Fingerboard_Markers_5f.zip", DOI: 10.5281/zenodo.10119514) is owned by Hart Honickman, and is licensed under Creative Commons Attribution 4.0 International. To view a copy of this license, view "Fingerboard_Markers_5f_LICENSE.pdf" or visit <a href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</a>. </p>
Electric Guitars Basic Data
<p>This dataset contains the basic information of the electric guitars that can be found on the alfasoni web portal, such as brand, name, price and the instrument description. This dataset is only for academic purpose. </p>
Multimodal Electric Guitar Data
<p>A dataset of thirty-six student and semiprofessional electric guitarists performing a set of basic sound-producing actions as well as free improvisations. The multimodal dataset consists of EMG and motion capture data; additionally, video and sound recordings of each performer were made.</p>
Sapek Guitar
i just finished this model. it only takes around 2 hour to created it The sape' (sampek, sampeh, sapek) is a traditional lute of many of the Orang Ulu or "upriver people", mainly the Kayan and Kenyah community who live in the longhouses that line the rivers of Central Borneo. Sapes are carved from a single bole of wood, with many modern instruments reaching over a metre in length. Source: Objaverse 1.0 / Sketchfab
Ganesh Guitar
3D model reflecting the underlying theme of cultural appropriation. Source: Objaverse 1.0 / Sketchfab
Cigar Box Guitar
A Cigar Box Guitar/Tenor Guitar made in maple wood. Reconstructed with CNR - Open Mesh Reconstructor (free photogrammetry software, after registration to the d4science.org platform) https://services.d4science.org/group/rprototypinglab/data-miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.OPEN_MESH_RECONSTRUCTOR_ADVANCED Citation and more information on the software: Coro, G., Palma, M., Ellenbroek, A., Panichi, G., Nair, T., & Pagano, P. (2019). Reconstructing 3D virtual environments within a collaborative e‐infrastructure. Concurrency and Computation: Practice and Experience, 31(11), e5028. https://doi.org/10.1002/cpe.5028 Source: Objaverse 1.0 / Sketchfab
Guitar
Just a lowpoly classic guitar. I made it for my game, but you also can use it. Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.