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8 results for “music generation”
EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation
<p>EMOPIA (pronounced ‘yee-mò-pi-uh’) dataset is a shared multi-modal (audio and MIDI) database focusing on perceived emotion in <strong>pop piano music</strong>, to facilitate research on various tasks related to music emotion. The dataset contains <strong>1,087</strong> music clips from 387 songs and <strong>clip-level</strong> emotion labels annotated by four dedicated annotators. </p> <p>For more detailed information about the dataset, please refer to our paper: <a href="https://arxiv.org/abs/2108.01374"><strong>EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation</strong></a>. </p> <p><strong>File Description</strong></p> <ul> <li><em><strong>midis/</strong></em>: midi clips transcribed using GiantMIDI. <ul> <li>Filename `Q1_xxxxxxx_2.mp3`: Q1 means this clip belongs to Q1 on the V-A space; xxxxxxx is the song ID on YouTube, and the `2` means this clip is the 2nd clip taken from the full song.</li> </ul> </li> <li><em><strong>metadata/</strong></em>: metadata from YouTube. (Got when crawling)</li> <li> <p><em><strong>songs_lists/</strong></em>: YouTube URLs of songs.</p> </li> <li> <p><em><strong>tagging_lists/</strong></em>: raw tagging result for each sample.</p> </li> <li> <p><em><strong>label.csv</strong></em>: metadata that records filename, 4Q label, and annotator.</p> </li> <li> <p><em><strong>metadata_by_song.csv</strong></em>: list all the clips by the song. Can be used to create the train/val/test splits to avoid the same song appear in both train and test.</p> </li> <li> <p><em><strong>scripts/prepare_split.ipynb:</strong></em> the script to create train/val/test splits and save them to csv files.</p> </li> </ul> <p>------</p> <p><strong>2.2 Update</strong></p> <ul> <li>Add tagging files in <em><strong>tagging_lists/</strong></em> that are missing in the previous version.</li> <li>Add <em><strong>timestamps.json</strong></em> for easier usage. It records all the timestamps in dict format. You can see <em><strong>scripts/load_timestamp.ipynb</strong></em> for the format example.</li> <li>Add <em><strong>scripts/timestamp2clip.py</strong></em>: After the raw audio are crawled and put in <em><strong>audios/raw</strong></em>, you can use this script to get audio clips. The script will read <em><strong>timestamps.json</strong></em> and use the timestamp to extract clips. The clips will be saved to <em><strong>audios/seg</strong> </em>folder.</li> <li>remove 7 midi files that were added by mistake, and also corrected the number in <em><strong>metadata_by_song.csv</strong></em>.</li> </ul> <p> </p> <p><strong>2.1 Update</strong></p> <p>Add one file and one folder:</p> <ul> <li><em><strong>key_mode_tempo.csv</strong></em>: key, mode, and tempo information extracted from files.</li> <li><strong><em>CP_events/</em></strong>: CP events used in our paper. Extracted using this <a href="https://github.com/YatingMusic/compound-word-transformer/blob/main/dataset/representations/uncond/cp/corpus2events.py">script</a>, and add the emotion event to the front.</li> </ul> <p>Modify one folder:</p> <ul> <li>The <strong><em>REMI_events/</em></strong> files in version 2.0 contain some information that is not related to the paper, so remove it.</li> </ul> <p> </p> <p><strong>2.0 Update</strong></p> <p>Add two new folders:</p> <ul> <li><strong><em>corpus/</em></strong>: processed data that following <a href="https://github.com/YatingMusic/compound-word-transformer/blob/main/dataset/Dataset.md">the preprocessing flow</a>. (Please notice that although we have <code>1078</code> clips in our dataset, we lost some clips during steps 1~4 of the flow, so the final number of clips in this <strong><code>corpus</code></strong> is <code>1052</code>, and that's the number we used for training the generative model.)</li> <li><strong><em>REMI_events/</em></strong>: REMI event for each midi file. They are generated using this <a href="https://github.com/YatingMusic/compound-word-transformer/blob/main/dataset/representations/uncond/remi/corpus2events.py">script</a>.</li> </ul> <p>-------- </p> <p> </p> <p> </p> <p> </p> <p><strong>Cite this dataset</strong></p> <pre><code>@inproceedings{{EMOPIA}, author = {Hung, Hsiao-Tzu and Ching, Joann and Doh, Seungheon and Kim, Nabin and Nam, Juhan and Yang, Yi-Hsuan}, title = {{MOPIA}: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation}, booktitle = {Proc. Int. Society for Music Information Retrieval Conf.}, year = {2021} }</code></pre>
Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls
<p>A sample from the paper 'Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls' conditioned on 1685 Germany.</p>
Exploring Conditioning for Generative Music Systems with Human-Interpretable Controls
<p>Performance RNN is a machine-learning system designed primarily for the generation of solo piano performances using an event-based (rather than audio) representation. More specifically, Performance RNN is a long short-term memory (LSTM) based recurrent neural network that models polyphonic music with expressive timing and dynamics (Oore et al., 2018). The neural network uses a simple language model based on the Musical Instrument Digital Interface (MIDI) file format. Performance RNN is trained on the <em>e-Piano Junior Competition Dataset </em>(International Piano e-Competition 2018), a collection of solo piano performances by expert pianists. As an artistic tool, one of the limitations of the original model has been the lack of useable controls. The standard form of Performance RNN can generate interesting pieces, but little control is provided over <em>what </em>specifically is generated. This paper explores a set of conditioning-based controls used to influence the generation process.</p> <p>Below, a brief description of the samples that accompany the paper are given:</p> <ul> <li><strong>sample_01.mp3: </strong>Composer latitude, longitude, and birth year conditioning. Conditioned on Germany in 1685.</li> <li><strong>sample_02.mp3: </strong>Composer latitude, longitude, and birth year conditioning. Conditioned on Poland in 1810.</li> <li><strong>sample_03.mp3: </strong>Composer latitude, longitude, and birth year conditioning. Conditioned on Poland in 1810.</li> <li><strong>sample_04.mp3: </strong>Tempo keyword conditioning. Conditioned to begin at adagio (very slow) and end at presto (very fast).</li> <li><strong>sample_05.mp3: </strong>Velocity conditioning. Conditioned to begin quiet, grow loud, and then end quietly.</li> <li><strong>sample_06.mp3: </strong>Velocity conditioning. Conditioned to begin quiet, grow loud, and then end quietly.</li> <li><strong>sample_07.mp3: </strong>Relative-position and composer conditioning. Conditioned to be in the style of Debussy and the beginning of a piece.</li> <li><strong>sample_08.mp3: </strong>Relative-position and composer conditioning. Conditioned to be in the style of Bach and the ending of a piece.</li> <li><strong>sample_09.mp3: </strong>Tempo keyword and velocity conditioning. Conditioned to begin slow and quiet and then become fast and loud.</li> <li><strong>sample_10.mp3: </strong>Composer conditioning. Conditioned on Ravel.</li> <li><strong>sample_11.mp3: </strong>Composer conditioning. Conditioned on Schumann.</li> <li><strong>sample_12.mp3: </strong>Velocity conditioning. Conditioned to begin quiet, grow loud, and then end quietly.</li> <li><strong>sample_13.mp3: </strong>Birth year conditioning. Conditioned on the 1600s.</li> <li><strong>sample_14.mp3: </strong>Composer latitude, longitude, and birth year conditioning. Conditioned on Spain in 1860.</li> </ul>
Film-Based Music Therapy With AI-Generated Verbal Guidance for Anxiety in Young Adults
ClinicalTrials.gov study NCT07359950. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Efficacy of Artificial Intelligence-Generated Music Therapy in Reducing Dental Anxiety Among Adults Undergoing Impacted Third Molar Extraction
ClinicalTrials.gov study NCT06998979. IPD Sharing: Not stated. Countries: 1. Publications: 0.
UMD-350MB: Refined MIDI Dataset for Symbolic Music Generation
<p><strong>UMD-350MB</strong></p> <p>The Universal MIDI Dataset 350MB (UMD-350MB) is a proprietary collection of 85,618 MIDI files curated for research and development within our organization. This collection is a subset sampled from a larger dataset developed for pretraining symbolic music models.</p> <p>The field of symbolic music generation is constrained by limited data compared to language models. Publicly available datasets, such as the Lakh MIDI Dataset, offer large collections of MIDI files sourced from the web. While the sheer volume of musical data might appear beneficial, the actual amount of valuable data is less than anticipated, as many songs contain less desirable melodies with erratic and repetitive events.</p> <p>The UMD-350MB employs an attention-based approach to achieve more desirable output generations by focusing on human-reviewed training examples of single-track melodies, chord progressions, leads and arpeggios with an average duration of 8 bars. This was achieved by refining the dataset over 24 months, ensuring consistent quality and tempo alignment. Moreover, the dataset is normalized by setting the timing information to 120 BPM with a tick resolution (PPQ) of 96 and transposing the musical scales to C major and A minor (natural scales).</p> <p><strong>Melody Styles</strong></p> <p>A major portion of the dataset is composed of newly produced private data to represent modern musical styles.</p> <ul> <li>Pop: 1970s to 2020s Pop music</li> <li>EDM: Trance, House, Synthwave, Dance, Arcade</li> <li>Jazz: Bebop, Ballad, Latin-Jazz, Bossa-Jazz, Ragtime</li> <li>Soul: 80s Classic, Neo-Soul, Latin-Soul</li> <li>Urban: Pop, Hip-Hop, Trap, R&B, Afrobeat</li> <li>World: Latin, Bossa Nova, European</li> <li>Other: Film, Cinematic, Game music and piano references</li> </ul> <p><em>Actual MIDI files are unlabeled for unsupervised training.</em></p> <p><strong>Dataset Access</strong></p> <p>Please note that this is a closed-source dataset with very limited access. Considerations for access include proposals for data augmentation, chord extraction and other enhancement methods, whether through scripts, algorithmic techniques, manual editing in a DAW or additional processing methods.</p> <p>For inquiries about this dataset, please email us.</p>
(Tiny) Music Score Generation Dataset
<p>This dataset is a much smaller version of the Music Score Generation Dataset from the thesis on automatic score-to-score music generation</p>
Music Score Generation Dataset
<p>This dataset was created for automatic score-to-score generation for piano.</p>
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International Brain Laboratory public data
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OpenNeuro
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