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9 results for “music genres”
Multi-modal dataset for music genre recognition based on six different modalities for LMD-aligned and SLAC datasets
<p>Multi-modal dataset for music genre recognition based on six different modalities for the LMD-aligned [1] and SLAC [2] datasets. Further details are provided in [3].</p> <p><strong>Descriptions of files</strong></p> <table> <thead> <tr> <th scope="col">Link</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_Filelist.arff">LMD-aligned_Filelist.arff</a></td> <td>File list with 1575 music tracks selected from the LMD-aligned dataset [1] with tagtraum genre annotations [4] (only a subset of LMD-aligned is used, which includes only pieces for which all six modalities were accessible, and which includes only well-represented genres)</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_ExtractedFeatures.tar.gz">LMD-aligned_ExtractedFeatures.tar.gz</a></td> <td>Raw audio signal and model-based features extracted with AMUSE [5]</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_ProcessedFeatures.tar.gz">LMD-aligned_ProcessedFeatures.tar.gz</a></td> <td>Processed features: audio signal and model-based features aggregated for 4 s time frames with 2 s step size / all other features (see the table below) with the same values for all time frames</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/LMD-aligned_Datasets.tar.gz">LMD-aligned_Datasets.tar.gz</a></td> <td>Training, optimization, and test datasets for 3 splits for the recognition of 5 genres in [3]</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_Filelist.arff">SLAC_Filelist.arff</a></td> <td>File list with 250 music tracks from the SLAC dataset [2] (genres and sub-genres are provided in the folder structure)</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_ExtractedFeatures.tar.gz">SLAC_ExtractedFeatures.tar.gz</a></td> <td>Raw audio signal and model-based features extracted with AMUSE [5]</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_ProcessedFeatures.tar.gz">SLAC_ProcessedFeatures.tar.gz</a></td> <td>Processed features: audio signal and model-based features aggregated for 4 s time frames with 2 s step size / all other features (see the table below) with the same values for all time frames</td> </tr> <tr> <td><a href="https://zenodo.org/record/5651429/files/SLAC_Datasets.tar.gz">SLAC_Datasets.tar.gz</a></td> <td>Training, optimization, and test datasets for 3 splits for the recognition of 5 genres and 10 sub-genres in [3]</td> </tr> </tbody> </table> <p><strong>Modalities and feature sub-groups</strong></p> <table> <thead> <tr> <th scope="col">Modality</th> <th scope="col">Sub-group</th> <th scope="col"> <p>Dimensions in processed</p> <p>features of LMD-aligned</p> </th> <th scope="col"> <p>Dimensions in processed</p> <p>features of SLAC</p> </th> </tr> </thead> <tbody> <tr> <td>Audio signal</td> <td>Low-level</td> <td>1-524</td> <td>1-524</td> </tr> <tr> <td>Audio signal</td> <td>Semantic</td> <td>525-810</td> <td>525-810</td> </tr> <tr> <td>Audio signal</td> <td>Structural complexity</td> <td>811-908</td> <td>811-908</td> </tr> <tr> <td>Model-based</td> <td>Instruments</td> <td>909-1018</td> <td>909-1018</td> </tr> <tr> <td>Model-based</td> <td>Moods</td> <td>1019-1146</td> <td>1019-1146</td> </tr> <tr> <td>Model-based</td> <td>Various</td> <td>1147-1402</td> <td>1147-1402</td> </tr> <tr> <td>Playlists</td> <td>Genres</td> <td>1403-1973</td> <td>1403-1973</td> </tr> <tr> <td>Playlists</td> <td>Styles</td> <td>1974-1695</td> <td>1974-1695</td> </tr> <tr> <td>Symbolic</td> <td>Pitch</td> <td>1696-1757</td> <td>1696-1757</td> </tr> <tr> <td>Symbolic</td> <td>Melodic</td> <td>1758-1781</td> <td>1758-1781</td> </tr> <tr> <td>Symbolic</td> <td>Chords</td> <td>1782-1836</td> <td>1782-1836</td> </tr> <tr> <td>Symbolic</td> <td>Rhythm</td> <td>1837-1935</td> <td>1837-1935</td> </tr> <tr> <td>Symbolic</td> <td>Tempo</td> <td>1936-1963</td> <td>1936-1963</td> </tr> <tr> <td>Symbolic</td> <td>Instrument presence</td> <td>1964-2441</td> <td>1964-2441</td> </tr> <tr> <td>Symbolic</td> <td>Instruments</td> <td>2442-2456</td> <td>2442-2456</td> </tr> <tr> <td>Symbolic</td> <td>Texture</td> <td>2457-2480</td> <td>2457-2480</td> </tr> <tr> <td>Symbolic</td> <td>Dynamics</td> <td>2481-2484</td> <td>2481-2484</td> </tr> <tr> <td>Album covers</td> <td>SIFT</td> <td>2485-2584</td> <td>2485-2584</td> </tr> <tr> <td>Lyrics</td> <td>jLyrics descriptors</td> <td>2585-2603</td> <td>2585-2671</td> </tr> <tr> <td>Lyrics</td> <td>Bag-of-Words</td> <td>2604-2703</td> <td> </td> </tr> <tr> <td>Lyrics</td> <td>Doc2Vec</td> <td>2704-2803</td> <td> </td> </tr> </tbody> </table>
Dataset from the ISMIR2020 article "Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation"
<p>We release the data required to reproduce the cross-lingual music genre translation experiments from the article <strong><em>Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation</em></strong> presented at the <a href="https://ismir.github.io/ISMIR2020/">ISMIR 2020</a> conference.</p> <p>More information about this data and how it should be used in the experiments can be found in the GitHub repository <a href="http://github.com/deezer/MultilingualMusicGenreEmbedding">deezer/MultilingualMusicGenreEmbedding</a>.</p> <p>Please cite our paper if you use the code or data in your work.</p>
MGD+: An Enhanced Music Genre Dataset with Success-based Networks
<p>This dataset is built by using data from Spotify. It provides a daily chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart. </p> <p>Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to March 2022 (downloaded from CSV files), considering 68 distinct markets.</p> <p>We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD+ also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:</p> <ul> <li><strong>Genre Networks:</strong> Success-based genre collaboration networks</li> <li><strong>Artist Networks:</strong> Success-based artist collaboration networks</li> <li><strong>Artists:</strong> Some artist data</li> <li><strong>Hit Songs:</strong> Hit Song data and features</li> <li><strong>Charts:</strong> Enhanced data from Spotify Daily Top 200 Charts</li> </ul>
Dataset from the EMNLP 2020 article "Modeling the Music Genre Perception across Language-Bound Cultures"
<p>We release the data required to reproduce the experiments from the article <em>Modeling the Music Genre Perception across Language-Bound Cultures</em> presented at the <a href="https://2020.emnlp.org">EMNLP 2020</a> conference.</p> <p>More information about this data and how it should be used in the experiments can be found in the GitHub repository <a href="https://github.com/deezer/CrossCulturalMusicGenrePerception">deezer/CrossCulturalMusicGenrePerception</a>.</p> <p>Please cite our paper if you use the code or data in your work.</p>
Music Genre fMRI Dataset - Derivatives
<p>This dataset contains preprocessed data from the Music Genre fMRI Dataset (<a href="https://openneuro.org/datasets/ds003720/versions/1.0.0">https://openneuro.org/datasets/ds003720/versions/1.0.0</a>). Experimental stimuli can be generated using GTZAN_Preprocess.py.</p> <p>References:</p> <p>1. Nakai, Koide-Majima, and Nishimoto (2021). Correspondence of categorical and feature-based representations of music in the human brain. Brain and Behavior. 11(1), e01936. https://doi.org/10.1002/brb3.1936</p> <p>2. Nakai, Koide-Majima, and Nishimoto (2022). Music genre neuroimaging dataset. Data in Brief. 40, 107675. https://doi.org/10.1016/j.dib.2021.107675</p>
The Influence of Different Music Genres on Hearing Performance in Noise
ClinicalTrials.gov study NCT06945588. IPD Sharing: NO. Countries: 1. Publications: 0.
Music Genre Stereotypes to Boost Relaxation in Chronic Pain Patients
ClinicalTrials.gov study NCT05979103. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Effects of Different Music Genres on Heart Rate Variability in Extremely and Very Low Birth Weight Newborns
ClinicalTrials.gov study NCT04049526. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Different Music Genres on Anxiety in Orthodontic Patients
ClinicalTrials.gov study NCT07267676. IPD Sharing: NO. Countries: 1. Publications: 0.
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