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38 results for “violin”
Sound samples for the evaluation of source-level blending between violins
<p>The repository includes monophonically rendered sound files used in the source-level blending evaluation. The sound samples provided are recorded from a violin ensemble performance at Detmold Concert House as a part of an investigation on the influence of acoustic environment on the impression of blending [1]. DPA 4099 clip-on microphones were used to capture individual violins in the performance.</p> <p>Each sound sample consists of two violin signals that were rendered by downmixing to a monophonic format at 44.1kHz/16-bit depth. The impression of blending between the two violins in each sample was rated by a group of trained listeners, and the results are provided in the file, 'Sound sample_description'. Please refer to the publication for more details on the performance of the violin ensemble. Also, please cite the publication if these samples are used for scientific evaluations.</p> <p>In addition to the sound samples, three Matlab figures that demonstrate the cluster distribution of MFCC features extracted from sound samples (that are classified into blended and non-blended classes) transformed using Principal component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-Stochastic Neighbourhood Embedding (t-SNE), are included for 3D visualization. This corresponds to Figure 9 in [2].</p> <p> </p> <p> [1] Jithin Thilakan and Malte Kob, “Evaluation of subjective impression of instrument blending in a string ensemble”, Fortschritte der Akustik - DAGA 2021 in Wien, pp. 524-527.</p> <p>[2] Jithin Thilakan, Balamurali BT, Jer-Ming Chen, Malte Kob, “Classification of the perceptual impression of source-level blending between violins in a joint performance”, currently under review at Acta Acustica.</p>
Violin MIDI Dataset
<h3>Violin MIDI Transcription Dataset from <strong><em><a title="High-Resolution Violin Transcription using Weak Labels" href="https://repositori.upf.edu/handle/10230/58121">High-Resolution Violin Transcription using Weak Labels</a></em></strong></h3> <h3><strong>Description</strong></h3> <p><strong>Overview</strong></p> <p>A descriptive transcription of a violin performance requires detecting not only the notes but also the fine-grained pitch variations, such as vibrato. Most existing deep learning methods for music transcription do not capture these variations and often need frame-level annotations, which are scarce for the violin. To enable the development of transcription methods tailored for the analysis of violin performances, this dataset includes MIDI files aligned to violin performances with 5.8 ms frame resolution and 10-cent frequency resolution, and includes pitch bends that represent fine-grained deviations such as vibrato and intonation choice.</p> <p><strong>Dataset Creation</strong></p> <p>The dataset consists of performances of three violin etude books by 22 violinists. The etude books included are:</p> <ul> <li>Paganini, Op. 1</li> <li>Wohlfahrt, Op. 35</li> <li>Kayser, Op. 20</li> </ul> <p>The original MIDI files pre-alignment are sourced from IMSLP and MuseScore (published under Creative Commons license). In the dataset, we provide these open-source MIDI files after they are aligned with the performance links from YouTube. Notably, each MIDI file includes pitch bends to offer a more realistic representation of the expressive performances, and to facilitate intonation analysis for pedagogical applications. The filenames are structured to include reconstructable links to the performances:</p> <p><code>{composer}_{catalog_number}_{performer}_{YouTube_ID}-{YouTube_start_sec}-{YouTube_end_sec}.mid</code></p> <p><strong>Contributions and Findings</strong></p> <p>We target the task of descriptive violin transcription with pitch bends, and demonstrate that our method (1) outperforms generic systems in the proxy tasks of violin transcription and pitch estimation, and (2) can automatically generate new training labels by aligning its feature representations with unseen scores. Additionally, we share our model along with 34 hours of score-aligned solo violin performance dataset, notably including the 24 Paganini Caprices.</p> <h3><strong>Citation</strong></h3> <p>If you find this dataset useful, please cite the following paper:</p> <blockquote> <p>Nazif Can Tamer, Yigitcan Özer, Meinard Müller, Xavier Serra, “High-Resolution Violin Transcription using Weak Labels”, in Proc. of the 24th Int. Society for Music Information Retrieval Conf., Milan, Italy, 2023.</p> </blockquote> <p><code>@inproceedings{tamer2023high,</code><br><code> title={High-Resolution Violin Transcription Using Weak Labels},</code><br><code> author={Tamer, Nazif Can and {\"O}zer, Yigitcan and M{\"u}ller, Meinard and Serra, Xavier},</code><br><code> booktitle={International Society for Music Information Retrieval Conference (ISMIR)},</code><br><code> year={2023}</code><br><code>}</code></p> <p> </p>
Bach Violin Dataset
<p>The Bach Violin Dataset is a collection of high-quality public recordings of Bach's sonatas and partitas for solo violin (BWV 1001–1006). The dataset consists of 6.5 hours of professional recordings from 17 violinists recorded in various recording setups. It also provides the reference scores and estimated alignments between the recordings and scores. For more information, please visit our project <a href="https://salu133445.github.io/bach-violin-dataset/">homepage</a>.</p>
Dataset of Violin Recordings with Spherical Microphone Arrays
<p>This data set contains simultaneous recordings of violin instrument sounds by multiple Spot microphones placed on a spherical skeleton.<br> There are 32 Spot microphones, recorded in 23 different ways of expression.</p> <p>Microphone ID</p> <p>Top Layer<br> 32<br> 27-28-29-30-31<br> 22-23-24-25-26<br> 17-18-19-20-21<br> 12-13-14-15-16<br> 7-8-9-10-11<br> 2-3-4-5-6<br> 1<br> Bottom Layer</p> <p> </p>
Seven Violin Dataset
<p>SEVEN VIOLIN DATASET.</p> <p>This dataset contains 7 individual notes played by the same violin. With ascending order, the notes involved are: F5, Ab5, A5, B5, Db6, E6, and Gb6. Each track was recorded in anechoic condictions and they were used for performance evaluation of a multipicth estimator and an audio source separation system, proposed in my PhD thesis [1], in Chapters 4 and 5 (Sections 4.4.2 and 5.8.1).</p> <p>[1] Delgado Castro, A. "Iterative separation of note events from single-channel polyphonic recordings". Ph.D. University of York. 2019.</p>
PFVN-synth: Synthesized Violin-Piano Ensemble Dataset
<p>A PFVN-synth dataset contains realistic instrumental triplet audios of piano, violin, and their mixture by rendering MIDI files with virtual instruments using musical scores of 45 different pieces by 23 classical composers with a total duration of 7 hours. All MIDI files were collected on the <a href="https://musescore.com">MuseScore website</a>. All tracks are rendered into monaural audio files with the standard CD quality: 44.1kHz, 16-bit. We used commercial virtual instruments to synthesize piano and violin. Specifically, we used ‘Bösendorfer Grand Piano’ in Apple Logic Pro and ‘SWAM Violin V3’ by Audio Modeling.</p> <p>It is divided into a train set containing 32 pieces with a duration of 5.8 hours, a validation set containing 3 pieces with a duration of 30 minutes, and a test set containing 10 pieces with a duration of 50 minutes so that the composers are not biased to the split sets.</p>
Violine 5446 von Gennaro Gagliano
<p>Foto der Violine 5446 (gebaut von Gennaro Gagliano, ca. 1745/1755), fotografiert am 15.8.2022 durch Barnes Ziegler</p>
Violin
District Museum in Tarnów Inventory number: MT-E/3270 interwar period (after 1918), Poland https://muzea.malopolska.pl/en/objects-list/1344 Source: Objaverse 1.0 / Sketchfab
Cycladic_Violin-shaped figurine
Early Bronze Age Early Cycladic I - Pelos Phase 3200 b.C. - 2800 b.C. Schematic violin-shaped figurine with a long rod-like projection denoting the head and neck, and flat body with two wide notches at the sides, forming the "waist" . The base of the neck is emphasized with an incised V motif. Violin-shaped figurines - thus named for obvious reasons - are the most common type of schematic representations of the human body in the Early Cycladic period and have several variations. They are usually small in size, only rarely exceeding 20 cm. in height. We do know that they represent female figures because several examples feature an incised pubic triangle or - less frequently - modelled breast. https://cycladic.gr/en/exhibit/ng1065-violoschimo-idolio Source: Objaverse 1.0 / Sketchfab
Violin without sides
Accession Number: MIMEd 5851. Mysterious violin without sides. Purchased by the University of Edinburgh in 2008. See: https://collections.ed.ac.uk/mimed/record/14679 (Model made by Veronica Wilson and Gaia Duberti; using CT scan data gathered by Dr Jonathan Santa Maria Bouquet; with assistance from Mike Boyd and UCreate Studio) Source: Objaverse 1.0 / Sketchfab
Stradivari Violin
Violin based on the "Messiah" Stradivari violin. For a school project. The mp3 was freely obtained from http://bestclassicaltunes.com/TuneDetails.aspx?TuneCode=VivaldiFourSeasonsSpringM1. Source: Objaverse 1.0 / Sketchfab
Скрипка Бромберга • Violin belonged to Bromberg
1880–1920 60 x 20 x 8.5 cm Скрипка принадлежала Вениамину Бромбергу, была с ним в годы заключения в Суздальском политизоляторе (1926–1931 гг). Является мануфактурной имитацией модели Гварнери. The violin belonged to Veniamin Bromberg and was with him during his imprisonment in the jail for political prisoners in Suzdal (1926–1931). The violin is a manufactured imitation of a Guarneri model. Source: Objaverse 1.0 / Sketchfab
electric violin
Just little electric violin. as a reference i used a model EV-505/mbk. Source: Objaverse 1.0 / Sketchfab
Motor performance in violin bowing: Effects of attentional focus on acoustical, physiological and physical parameters of a sound-producing action
<p>Violin bowing is a specialised sound-producing action, which may be affected by psychological performance techniques. In sport, attentional focus impacts motor performance, but limited evidence for this exists in music. We investigated the effects of attentional focus on acoustical, physiological, and physical parameters of violin bowing in experienced and novice violinists. Attentional focus significantly affected spectral centroid, bow contact point consistency, shoulder muscle activity, and novices’ violin sway. Performance was most improved when focusing on tactile sensations through the bow (somatic focus), compared to sound (external focus) or arm movement (internal focus). Implications for motor performance theory and pedagogy are discussed.</p>
Effects of Attentional Focus on Motor Performance and Physiology in a Slow-Motion Violin Bow-Control Task: Evidence for the Constrained Action Hypothesis in Bowed String Technique
<p>The constrained action hypothesis states that focusing attention on action outcomes rather than body movement improves motor performance. Dexterity of motor control is key to successful music performance, making this a highly relevant topic to music education. We investigated effects of focus of attention (FOA) on motor skill performance and EMG muscle activity in a violin bowing task among experienced and novice upper strings players. Following a pedagogically informed exercise, participants attempted to produce single oscillations of the string at a time under three FOA: internal (on arm movement), external (on sound produced), and somatic (on string resistance). Experienced players’ number of bow slips was significantly reduced under somatic focus relative to internal, although number of successful oscillations was not affected. Triceps electromyographic activity was also significantly lower in somatic compared to internal foci for both expertise groups, consistent with physiological understandings of FOA effects. Participants’ reported thoughts during the experiment provided insight into whether aspects of constrained action may be evident in performers’ conscious thinking. These results provide novel support for the constrained action hypothesis in violin bow control, suggesting a somatic FOA as a promising performance-enhancing strategy for bowed string technique.</p>
Tempo in Recordings of Beethoven's V. and IX. Symphony and Tchaikowsky's Violin Concerto
<p>This set contains tempo data of:</p> <ul> <li>142 recordings (1910–2020) of Beethoven's Fifth Symphony op. 67 (two versions: one containing the data as extracted from Laubhold's 2014 thesis, comprising 108 recordings 1910–2009 ["MIT_DATENLAUBHOLD"]; one adding data of another 34 recordings 2010–2020) </li> <li>78 recordings (1923–2021) of Beethoven's Ninth Symphony op. 125</li> <li>100 recordings (1988–2017) of Tchaikowsky's Violin Concerto op. 35 </li> </ul> <p>This dataset is part of the upcoming publication:</p> <ul> <li>Caskel, Julian: [Von Mutter bis Nelsons (Generation 1960–1980)], in: Loesch, Heinz von, Wolf, Rebecca, Ertelt, Thomas (Ed.), <em>Geschichte der musikalischen Interpretation im 19. und 20. Jahrhundert</em>. Bd. 4: <em>Personen – Stile – Konzepte</em>, Heidelberg: J.B. Metzler 2024, pp. tba. </li> <li>Laubhold's Beethoven V. data have been extracted from: Lars Laubhold, <em>Von Nikisch bis Norrington. Beethovens 5. Sinfonie auf Tonträger. Ein Beitrag zur Geschichte der musikalischen Interpretation im Zeitalter ihrer technischen Reproduzierbarkeit</em>, München 2014. </li> </ul>
Online Tone Manipulation in Violin Performance: An ERP and ERSP study
<p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>Data and Software used in <br> Online Tone Manipulation in Violin Performance: An ERP and ERSP Study.<br> <em>Ángel David Blanco, Jordi Costa-Faidella, Alfonso Pérez, David Dalmazzo, Rafael Ramirez, Iria SanMiguel</em><br> (not published at this moment)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>FILES:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1. Online_Tone_Manipulation_Violin_DATA.rar</strong></p> <p>In this compressed file we found 3 folders:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1.1 Raw_data</strong>: raw data of the participants of the experiment.</p> <p>Inside we find 16 folders. Each one contains the raw data of each participant: SXX (where XX is the code assigned to each subject).<br> Data from participants S01 and S11 are missing due to technical problems.</p> <p>Each folder contains:</p> <p>audio: This folder contains the audio recorded during each block of the session.<br> sXX: This folder contains the EEG files recorded during each block of the session.<br> tony: This folder contains the tony and excel files with the information about the audio onsets and the onsets of corrective movements.</p> <p>We also find 2 matlab scripts:</p> <p>main_final.m: This script creates one *.set file per block with the EEG data and the audio markers for each event. <br> main_EEG.m This script creates the Merged_Datasets.set file with the data from all the blocks. It also cleans the data from noise artifacts that were previously visual inspected.<br> It also computes the average reference, filters the Data, computes ICA and removes those components related with ocular activity. <br> It also creates te SXX_MergedDatasets_filt25_ICprun.set and the SXX_MergedDatasets_filt50_ICprun_TF.set</p> <p>Those files can already be found inside each folder. </p> <p>SXX_MergedDatasets_filt25_ICprun.set: This file contains the data for the ERPs already processed (pass band filter 1-25Hz). <br> SXX_MergedDatasets_filt25_ICprun_TF.set: This file contains the data for the ERSPs already processed (pass band filter 1-50Hz).</p> <p>RECODED TRIGGERS <br> (Based on audio onsets and logfiles)<br> Hundreds: TASK<br> Tens: FEEDBACK<br> Units: ORDER<br> 0: Reference<br> 100: Active<br> 200: Replayed<br> 300: Manipulated Active<br> 400: Post-error manipulation Active<br> 500: Non-manipulated active<br> 600: Manipulated Replayed<br> 700: Post-error manipulated Replayed<br> 800: Non-manipulated Replayed<br> 900: Onset End Correction Active<br> 1000: Onset End Correction Passive<br> 10: Open-String Note<br> 20:In Tune ONSET<br> 30: Mistuned ONSET <br> 40: In Tune STABLE<br> 50: Mistuned STABLE<br> 60: Notes with correction ONSET (All)<br> 70: Mistuned notes with correction ONSET<br> 80: Mistuned notes without correction ONSET<br> 1: Low (15-30c)<br> 2: LowHigh(30-50c)<br> 3: Middle (50-70c)<br> 4: MiddleHigh(70-100)<br> 5: High (>100)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p>In Raw_data we can also found two scripts</p> <p>load_participants.m: This script executes the main_final.m script for each participant.<br> load_participants_EEG.m: This script executes the main_EEG.m script for each participant</p> <p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>1.2 ERPs:</strong></p> <p>Inside this folder we find 3 more folders:</p> <p>Active: Contains the *.set files with the ERPs for each event of interest inside the Active condition.<br> Replayed (Passive): Contains the *.set files with the ERPs for each event of interest inside the Replayed condition.<br> Reference Melody: Contains the *.set files with the ERPs of the reference melody.</p> <p>Events of interest in the names of each Folder:<br> XXXX_Tuned: tuned notes<br> XXXX_Mistuned: notes with an error higher than 30 cents.<br> XXXX_nonman: nonmanipulated<br> XXXX_man: manipulated<br> XXXX_postman: postmanipulated<br> XXXX_Corr_Low: Trials with slow corrective movements (>350 ms) <br> XXXX_Corr_Medium: Trials with medium corrective movements (250-350ms)<br> XXXX_Corr_High: Trials with fast corrective movements (<250ms)<br> XXXX_Low: low error (15-30 cents)<br> XXXX_Medium: Medium error (30-50 cents)<br> XXXX_Medium_High: Medium High error (50-70 cents)<br> XXXX_High_High: High errors (>70 cents)</p> <p><br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> <br> <strong>1.3 ERSPs:</strong></p> <p>ERSPs of Active Tuned and Mistuned and Replayed Tuned and Mistuned in MATLAB Data files.</p> <p>Inside each file we can find the ERSPs and the ITC for different electrodes:</p> <p>ersp_XX: where XX is the name of the electrode (C3,C4,CP3,CP4)..<br> itc_XX: where XX is the name of the electrode (C3,C4,CP3,CP4)..</p> <p>both the ersp_XX and the itc_XX are three-dimensional matrices:</p> <p>frequencies (30 points) X time (200 points) X participants (15 subjects).</p> <p>The frequencies and times variables contain an array with the information of the frequency value (Hz) and time value (ms) for each point.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2. Online_Tone_Manipulation_Violin_STIMULI_and_MAX_software.rar</strong></p> <p>In this compressed file we found two folders:</p> <p><br> <strong>2.1 Online_Tone_Manipulation_System_in_Max folder</strong></p> <p>This folder contains the system in Max that allows us to manipulate the pitch of the played note in the melody.</p> <p>recording_session.maxpat: open this file to access the system.<br> random_file.csv: file which contains the order of the melodies reproduced to the participants, the note which has to receive the manipulation, and the direction of the manipulation (1 up, 0 down).</p> <p>We can also find two folders:</p> <p>audio: the audio of the participant for each block is recorded and saved inside this folder<br> New_generated_melodies: This folder needs to contain the melodies reproduced to the participant during the experiment</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2 Stimuli folder</strong></p> <p>This folder contains three folders:</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.1 Generated_Scores folder</strong></p> <p>This folder contains the code and which generates the score images used during the experiment.</p> <p>Inside the folder we can find:</p> <p>generate_scores.m: Script used to generate the score images</p> <p>New_generated_scores folder: This folder contains the XML code and the *.jpg file for each score.</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.2 Screen</strong></p> <p>This folder contains the code used to deliver the visual information to the participant during the session and also the clicks sent to the DSP computer and the markers to the EEG computer via parallel port.<br> The random_file.csv inside this folder has to be the same that the one contained inside the Online_Tone_Manipulation_System_in_Max.</p> <p>Inside this folder we can find:</p> <p>Violin_screen_Brainlab.m: script with the code which has to be executed to start delivering the visual instructions to the participants</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p> <p><strong>2.2.3 Violin_Sample_sounds</strong></p> <p>Inside this folder we can find two folders:</p> <p>New_generated_melodies: contains the final generated melodies of the experiment<br> Original_Sounds: contains the original sounds used to generate the rest of the melodies of the experiment</p> <p>We can also find two important scripts:</p> <p>Generate_audios: this script generates the different melodies of the experiment from the original sounds.<br> Randomize_audios_new: this script generates the random_file.csv with the random order of the melodies together</p> <p><br> </p>
Mathias1211/Violin_Boundary: Violin NURBS Curve
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Indigenous with violin
This model is a high quality scan. This scan was made using the Peel 3d scan. This scan has 4096x4096 texture. You can use this model in most of projects. Source: Objaverse 1.0 / Sketchfab
"Polish" violin
ID no.: MRG PTTK; KW no.: 752 https://muzea.malopolska.pl/en/objects-list/148 Museum: The Ignacy Łukasiewicz Regional Museum of Polish Tourism and Sightseeing Society in Gorlice Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
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