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3 results for “beat tracking”
Uruguayan candombe drumming - beat and downbeat tracking data set
<p>Uruguayan Candombe drumming - beat and downbeat data set</p> <p><br>===========<br>Description<br>===========</p> <p>This dataset includes more than 2 hours of Candombe recordings, with annotated beats and downbeats. It features 35 complete performances by renowned players, playing in groups of three to five drums. A total of 26 tambor players from various generations took part, representing the three key traditional Candombe styles. These recordings were created in a studio setting over a period of more than two decades as part of musicological research. The sessions from 1992 and 1995 were produced by Luis Jure, and the session from 2014 was produced by Luis Jure and Martín Rocamora.</p> <p>The audio files are in stereo with a sampling rate of 44.1 kHz and 16-bit precision. An expert annotated the location of beats and downbeats, totaling more than 4700 downbeats. The audio is provided in flac format and the annotations are in .csv files. The values in the first column of the CSV file represent the time instants of the beats. The numbers in the second column indicate both the bar number and the beat number within the bar. For example, 1.1, 1.2, 1.3, and 1.4 represent the four beats of the first bar. Therefore, each label ending in .1 indicates a downbeat. Another set of annotations is provided as .beats files in which the bar numbers are removed.</p> <p><br>========<br>Citation<br>========</p> <p>This dataset was released with the publication of the following paper. So if you find the dataset useful and want to reference it in your publications, please cite it.</p> <p>"Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming”. Leonardo Nunes, Martín Rocamora, Luis Jure, Luiz W. P. Biscainho. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), pages 264-270, Málaga, Spain, 26-30 October, 2015.</p> <p>@inproceedings{Nunes2015,<br> author = {Leonardo Nunes and Martín Rocamora and Luis Jure and Luiz W. P. Biscainho},<br> title = {{Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming}},<br> booktitle = {Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015)},<br> month = {Oct.},<br> address = {Málaga, Spain},<br> pages = {264--270},<br> year = {2015}<br>}</p> <p><br>========<br>Candombe<br>========</p> <p>Candombe is a vital part of Uruguayan popular culture, with thousands of practitioners and its rhythm influencing various genres of popular music. In 2009, UNESCO recognized it as part of the Intangible Cultural Heritage of Humanity. While it originated in Uruguay, Candombe has its roots in the culture brought by African slaves in the 18th century. Over time, it has evolved to incorporate the descendants of European immigrants and has become a part of the entire society. Candombe drumming, with its unique rhythm, is the essential element of this tradition, which also includes dancing, symbolic characters, and costumes.</p> <p>The drum used in Candombe is called tambor, which is Spanish for "drum." There are three different sizes: chico (small), repique (medium), and piano (big). Each size has its own unique sound, ranging from high to low frequency, and its own specific rhythmic patterns. All three drums are played with a stick in the dominant hand and the other one hitting the skin directly. The stick is also used to hit the shell when playing the clave or madera pattern. A minimal ensemble of drums (cuerda de tambores) must have at least one of each of the three drums. During a llamada de tambores, the ensemble usually consists of around 20 to 60 drums. When marching, the players walk forward with short steps synchronized with the beat, which is important for embodying the rhythm, even though it is not audible.</p> <p>===============<br>Acknowledgments<br>===============</p> <p>This work was partially supported by the funding agency Comisión Sectorial de Investigación Científica, Universidad de la República, Uruguay, and by the Department of Culture of the Municipality of Montevideo.</p> <p>This is the complete list of performers, in alphabetical order: Mariano Barroso, Eduardo 'Cacho' Giménez, Eduardo 'Malumba' Giménez, Francisco Giménez, José Luis Giménez, Jorge 'Foqué' Gómez, José Pedro 'Perico' Gularte, Luis 'Pocholo' Maciel, Julio Magariños, Raúl 'Neno' Magariños, Marcelo Magariños, Javier 'Cerdo' Martirena, Wilson Martirena, Eduardo 'Tierra' Nilo, Sergio Ortuño, Fernando 'Lobo' Núñez, Edinson 'Palo' Oviedo, Gustavo Oviedo, Egdardo Pintos, Luis 'Mocambo' Quiroz, Rodolfo 'Pelado' Rodríguez, Fernando 'Hurón' Silva, Juan Silva, Raúl Silva, Waldemar 'Cachila' Silva, and Héctor Manuel Suárez.</p>
Beat This! Spectrograms for Beat and Downbeat Tracking
<p>This collection contains mel spectrograms and annotations of 16 datasets for beat and downbeat tracking. All datasets have been used in "<a href="https://arxiv.org/abs/2407.21658">Beat This! Accurate beat tracking without DBN postprocessing</a>" (Foscarin/Schlüter/Widmer, ISMIR 2024) and prior publications by other authors, but for many of these datasets, audio data is not publicly available. By publishing the spectrograms, we invite other researchers to improve the state of the art in beat and downbeat tracking.</p> <h3>Datasets</h3> <p>Spectrograms for the following datasets are included in the collection:</p> <ul> <li><a href="https://github.com/fosfrancesco/asap-dataset">asap</a>: "ASAP: a dataset of aligned scores and performances for piano transcription" (Foscarin et al., ISMIR 2020)</li> <li>ballroom: "An experimental comparison of audio tempo induction algorithms" (Gouyon et al., TASLP 2006) for the audio and "Rhythmic Pattern Modeling for Beat and Downbeat Tracking in Musical Audio" (Krebs/Böck/Widmer, ISMIR 2013) for the annotations</li> <li>beatles: "Evaluation methods for musical audio beat tracking algorithms" (Davies/Degara/Plumbley, Tech. Rep., QMU, 2019)</li> <li><a href="https://www.eumus.edu.uy/candombe/datasets/ISMIR2015/dataset.html">candombe</a>: "Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming" (Nunes et al., ISMIR 2015) </li> <li>filosax: "Filosax: A dataset of annotated jazz saxophone recordings" (Foster/Dixon, ISMIR 2021)</li> <li><a href="https://magenta.tensorflow.org/datasets/groove" rel="nofollow">groove_midi</a>: "Learning to groove with inverse sequence transformations" (Gillick et al., ICML 2019)</li> <li>gtzan: "Musical genre classification of audio signals" (Tzanetakis/Cook, TSAP 2002) for the audio and "Swing ratio estimation" (Marchand/Peters, DAFx 2015) for the annotations</li> <li><a href="https://zenodo.org/records/3371780">guitarset</a>: "GuitarSet: A dataset for guitar transcription" (Xi et al., ISMIR 2018)</li> <li>hainsworth: "Particle filtering applied to musical tempo tracking" (Hainsworth/Macleod, JASP 2004)</li> <li>harmonix: "The Harmonix set: Beats, downbeats, and functional segment annotations of western popular music" (Nieto et al., ISMIR 2019) for the original and "Modeling Beats and Downbeats with a Time-Frequency Transformer" (Hung et al., ICASSP 2022) for the version included here</li> <li>hjdb: "One in the jungle: Downbeat detection in hardcore, jungle, and drum and bass" (Hockman/Davies/Fujinaga, ISMIR 2012)</li> <li>jaah: "Audio-aligned jazz harmony dataset for automatic chord transcription and corpus-based research" (Eremenko et al., ISMIR 2018)</li> <li><a href="https://staff.aist.go.jp/m.goto/RWC-MDB/" rel="nofollow">rwc</a>: "RWC music database: Popular, classical and jazz music databases" (Goto et al., ISMIR 2002) for the audio and "AIST annotation for the RWC music<br>database" (Goto, ISMIR 2006) for the annotations</li> <li>simac: "A computational approach to rhythm description — Audio features for the computation of rhythm periodicity functions and their use in tempo induction and music content processing" (Gouyon, PhD thesis, UPF, 2005)</li> <li><a href="https://joserzapata.github.io/publication/selective-sampling-beat-tracking/">smc</a>: "Selective sampling for beat tracking evaluation" (Holzapfel et al., TASLP 2012)</li> <li>tapcorrect: "Towards Automatically Correcting Tapped Beat Annotations for Music Recordings" (Driedger et al., ISMIR 2019)</li> </ul> <p>If given, links in the above list point to locations for obtaining the original audio.</p> <h3>Annotations</h3> <p>The corresponding annotations are available on <a href="https://github.com/CPJKU/beat_this_annotations">https://github.com/CPJKU/beat_this_annotations</a>. A snapshot of v1.0 is included in this collection as <code>beat_this_annotations.zip</code>, but you may want to use a later release.</p> <h3>Spectrograms</h3> <p>Spectrograms are computed from monophonic audio at a sample rate of 22050 Hz with a window size of 1024 and hop size of 441 samples (yielding 50 frames per second), processed with a mel filterbank of 128 bands from 30 Hz to 11 kHz, and magnitudes scaled with ln(1+1000x). They are provided in half-precision floating-point format. Spectrograms can be reproduced with torchaudio 2.3.1 from a 22050 Hz waveform tensor (resampled with <code><a href="https://python-soxr.readthedocs.io/en/latest/soxr.html#soxr.resample">soxr.resample()</a></code>, if needed) via:</p> <pre><code>melspect = torchaudio.transforms.MelSpectrogram(sample_rate=22050, n_fft=1024, hop_length=441, f_min=30, f_max=11000, n_mels=128, mel_scale='slaney', normalized='frame_length', power=1)(waveform).mul(1000).log1p()</code></pre> <h3>Format</h3> <p>For each dataset, a compressed .zip file is provided, which in turn holds an uncompressed .npz file. The .npz file holds a set of numpy arrays in subdirectories named after the annotations. Each subdirectory contains a spectrogram of the original audio file ("track.npy"), 11 pitch-shifted versions from -5 to +6 semitones ("track_ps-5.npy" to "track_ps6.npy") and 10 time-stretched versions from -20% to +20% ("track_ts-20.npy" to "track_ts20.npy"), except for <code>gtzan.npz</code>, which is designated for testing and only holds the original audio files. The .npz files can be loaded in numpy via <code><a href="https://numpy.org/doc/2.0/reference/generated/numpy.load.html">np.load()</a></code>, or unzipped into a set of .npy files that can again be loaded via <a href="https://numpy.org/doc/2.0/reference/generated/numpy.load.html"><code>np.load()</code></a>. We also provide <a href="https://github.com/CPJKU/beat_this/blob/main/beat_this/dataset/mmnpz.py">code to load .npz files as memory maps</a> for more efficiency.</p>
Effects of Exposure to a Single-electrode Electroencephalography-guided Binaural Beat Audio Track on Sustained Attention and Subjective Well-being Among Healthy Adults
ClinicalTrials.gov study NCT07165899. IPD Sharing: NO. Countries: 1. Publications: 0.
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