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5 results for “Million song dataset”
ESSENTIA analysis of audio snippets from the Million Song Dataset Taste Profile subset
<p>This upload includes the ESSENTIA analysis output of (a subset of) song snippets from the Million Song Dataset, namely those included in the Taste Profile subset. The audio snippets were collected from 7digital.com and were subsequently analyzed with ESSENTIA 2.1-beta3. Pre-trained SVM models provided by the ESSENTIA authors on their website were applied.</p> <p>The file <strong>msd_song_jsons.rar </strong>contains the ESSENTIA analysis output after applying the SVM models for highlevel feature extraction. Please note that these are 204317 files.</p> <p>The file <strong>msd_played_songs_essentia.csv.gz </strong>contains all one-dimensional real-valued fields of the jsons merged into one csv file with 204317 rows.</p> <p>The full procedure and subsequent analysis is described in</p> <p>Fricke, K. R., Greenberg, D. M., Rentfrow, P. J., & Herzberg, P. Y. (2019). Measuring musical preferences from listening behavior: Data from one million people and 200,000 songs. <em>Psychology of Music</em>, 0305735619868280.</p>
MSD-A: Million Song Dataset for Artists
<p>The MSD-A is a dataset related to the Million Song Dataset (MSD). It is a collection of artist tags and biographies gathered from Last.fm for all the artists that have songs in the MSD. In addition, the MSD Taste Profile (recommendation dataset) is adapted to artists.</p> <p>We provide the biographies, tags, data splits, and feature embeddings to reproduce the experiments from the paper:</p> <p>Oramas S., Nieto O., Sordo M., & Serra X. (2017) A Deep Multimodal Approach for Cold-start Music Recommendation. https://arxiv.org/abs/1706.09739</p> <p>Source code is available at https://github.com/sergiooramas/tartarus</p> <p>The file dlrs-data.tar.gz in this zenodo version is corrupted. You can download the good file in this link:</p> <p>https://drive.google.com/open?id=0B-oq_x72w8NUbUpkMzZSc1JPd28</p>
MSD-I: Million Song Dataset with Images for Multimodal Genre Classification
<p>The Million Song Dataset (https://labrosa.ee.columbia.edu/millionsong/) is a collection of metadata and precomputed audio features for 1 million songs. Along with this dataset, a dataset with annotations of 15 top-level genres with a single label per song was released. In our work, we combine the CD2c version of this genre datase (http://www.tagtraum.com/msd_genre_datasets.html) with a collection of album cover images. </p> <p><br> The final dataset contains 30,713 tracks from the MSD and their related album cover images, each annotated with a unique genre label among 15 classes. Based on an initial analysis on the images, we identified that this set of tracks is associated to 16,753 albums, yielding an average of 1.8 songs per album.</p> <p>We randomly divide the dataset into three parts: 70% for training, 15% for validation, and 15% for test, with no artist and album overlap across these sets. This is crucial to avoid possible overfitting, as the classifier may learn to predict the artist instead of the genre. </p> <p> </p> <p>Content:</p> <p>MSD-I dataset (mapping, metadata, annotations and links to images)<br> Data splits and feature vectors for TISMIR single-label classification experiments </p> <p>These data can be used together with the Tartarus deep learning python module https://github.com/sergiooramas/tartarus.</p> <p> </p> <p>Scientific References:</p> <p>Please cite the following paper if using MSD-I dataset or Tartarus software.</p> <p>Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1).</p>
Hit Song Prediction (Million Song Dataset and Audio Features)
<p><strong>Hit Song Prediction Dataset</strong></p> <p>This dataset is based on the Million Song Dataset (MSD), which contains one million songs that are representative for western commercial music released between 1922 and 2011. The dataset contains release year information for 515,576 of the MSD songs. Please refer to http://millionsongdataset.com/ for further information on the million song dataset.</p> <p>For our hit song prediction experiments, we extract high- and low-level audio features using the Essentia toolkit (cf. https://essentia.upf.edu/). For the high-level features, we make use of the pre-trained classifiers as provided by Essentia. For a detailed description of the features, please visit the Essentia documentation.</p> <p><br> The dataset hence contains:</p> <ul> <li><strong>Audio features</strong>: the compressed msd_audio_features.tar.gz file contains the low- and high-level features for each track, stored as json files. Please note that we organize all MSD audio feature files based on the track's identifier with one folder holding all tracks with the same first letter of the track identifier to keep the files manageable. For each track, we provide two files: one containing the high-level and one containing the low-level features extracted by Essentia.</li> <li><strong>Billboard data:</strong> the folder billboard_data contains two files: msd_bb_matches.csv contains information about the MSD tracks that were also featured in the Billboard Hot 100 charts. Here, we provide the MSD id, Echo Nest id, artist name, track title, release year, peak position in Billboard charts and the number of weeks in the charts. The second file, msd_bb_non_matches.csv contains meta-information about the tracks of the MSD that were not featured in the Billboard Hot 100 and hence were used as negative samples. Here, we provide the MSD id, Echo Nest id, artist name, track title and the release year.</li> </ul> <p><br> If you make use of the dataset, please kindly cite the following paper:</p> <p>Eva Zangerle, Michael Vötter, Ramona Huber, and Yi-Hsuan Yang. Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019), 2019.</p> <p><br> @inproceedings{zangerle_ismir19,<br> title = {{Hit Song Prediction: Leveraging Low- and High-Level Audio Features}},<br> author = {Eva Zangerle and Ramona Huber and Michael V\"{o}tter and Yi-Hsuan Yang},<br> year = {2019},<br> booktitle = {{Proceedings of the 20th International Society for Music Information Retrieval Conference 2019 (ISMIR 2019)}},<br> }</p>
The Million Song Dataset
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