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3 results for “beijing opera”
Beijing Opera Percussion Instrument Dataset
<p>The Beijing Opera percussion instrument dataset is a collection of audio examples of individual strokes spanning the four percussion instrument classes used in Beijing Opera (Jingju, 京剧).</p> <p>Beijing Opera uses six main percussion instruments that can be grouped into four classes: </p> <ol> <li><strong>Bangu</strong> (Clapper-drum) consisting of Ban (the clapper, a wooden board-­shaped instrument) + danpigu (a wooden drum struck by two wooden sticks)</li> <li><strong>Naobo</strong> (Cymbals) consisting of two cymbal instruments Qibo+Danao</li> <li><strong>Daluo</strong>: Large gong</li> <li><strong>Xiaoluo</strong>: Small gong</li> </ol> <p><strong>Audio content</strong></p> <p>The dataset provides audio examples for each of these instrument classes.</p> <p>The audio examples were recorded under studio conditions by Mi Tian at the <a href="http://c4dm.eecs.qmul.ac.uk/">Centre for Digital Music</a>, Queen Mary University of London, UK in September 2013 using an AKG C414 microphone. The audio was sampled at 44.1 kHz and stored as 16 bit wav files. The instruments were played by Ying Wan of the London Jing Kun Opera Association. Unlike some instruments that can be tuned, these percussion instruments are made from metal casting. Thus, there can be subtle timbral differences even across different instruments of the same kind. For each of these instruments, we used 2-3 individual instruments to record the samples, hoping to achieve a better timbre coverage. Further, audio samples were recorded using different playing techniques for each instrument.</p> <p>The dataset can be used for training models for each percussion instrument class. </p> <p>Each audio file is named as, </p> <pre><code><InstrumentClass>_<InstanceNumber>.wav</code></pre> <p><strong>Using this dataset</strong></p> <p>Please cite the following paper if you use this dataset in your work:</p> <blockquote> <p>Mi Tian, Ajay Srinivasamurthy, Mark Sandler, and Xavier Serra, "A Study of Instrument-wise Onset Detection in Beijing Opera Percussion Ensembles", in Proceedings of ICASSP 2014, Florence, Italy, May 2014.</p> </blockquote> <p><a href="https://doi.org/10.1109/ICASSP.2014.6853981">https://doi.org/10.1109/ICASSP.2014.6853981</a></p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p><strong>Contact</strong></p> <p>If you have any questions or comments about the dataset, please feel free to write to us: </p> <p>Mi Tian ( m.tian@qmul.ac.uk ) or Ajay Srinivasamurthy ( ajays.murthy@upf.edu)</p> <p> </p> <p><a href="http://compmusic.upf.edu/bo-perc-dataset">http://compmusic.upf.edu/bo-perc-dataset</a></p>
Jingju (Beijing opera) "Remorse at death" multi-camera teaching and performing videos
<p>此文件包含梅派京剧曲目《生死恨》多视角教学、演出视频,由中国戏曲学院摄制。 本素材属于由中国戏曲学院张晶老师负责的北京市社会科学基金研究基地项目 -- 《梅派唱腔的音视频与电脑辅助教学研究》(项目号 16JDYTA016)成果的一部分。Music Technology Group, Universitat Pompeu Fabra, Barcelona作为项目参与方负责讲此视频素材封装在iOS app中。</p> <p>This file contains the multi-camera teaching and performing videos of Mei school jingju (Beijing opera) play "Remorse at death", shot and edited by National Academy of Chinese Theatre Arts (NACTA). These video materials are a part of the outcome of the Beijing City Social Science Foundation project -- "Audio-Visual and computer-aided research on Mei school singing teaching" (Num. 16JDYTA016), directed by professor ZHANG Jin in NACTA. As a collaborator, Music Technology Group, Universitat Pompeu Fabra, Barcelona is in charge of encapsulating these videos materials into an iOS app.</p>
Beijing Opera Percussion Pattern Dataset
<p>The Beijing Opera Percussion Pattern (BOPP) dataset is a collection of audio examples of percussion patterns played by the percussion ensemble in Beijing Opera (Jingju, 京剧). The percussion ensemble in Jingju plays a set of pre-defined and labeled percussion patterns, which serve many functions. The percussion patterns can be defined as sequences of strokes played by different combinations of the percussion instruments, and the resulting variety of timbres are transmitted using oral syllables as mnemonics. More information on the percussion instruments used in Beijing Opera can be found at <a href="http://compmusic.upf.edu/examples-percussion-bo">http://compmusic.upf.edu/examples-percussion-bo</a>.</p> <p>The dataset presented here was used as the training dataset in the referenced paper. A detailed description of percussion patterns in Jingju can also be found in it.</p> <p><strong>DATASET</strong></p> <p>The dataset is a collection of 133 audio percussion patterns spanning five different pattern classes as described below. The scores for the patterns and additional details about the patterns are at: <a href="http://compmusic.upf.edu/bo-perc-patterns">http://compmusic.upf.edu/bo-perc-patterns</a></p> <p><strong>Audio Content</strong></p> <p>The audio files are short segments containing one of the above mentioned patterns. The audio is stereo, sampled at 44.1 kHz, and stored as wav files. The segments were chosen from the introductory parts of arias. The recordings of arias are from commercially available releases spanning various artists. The audio and segments were chosen carefully by a musicologist to be representative of the percussion patterns that occur in Jingju. The audio segments contain diverse instrument timbres of percussion instruments (though the same set of instruments are played, there can be slight variations in the individual instruments across different ensembles), recording quality and period of the recording. Though these recordings were chosen from introductions of arias where only percussion ensemble is playing, there are some examples in the dataset where the melodic accompaniment starts before the percussion pattern ends. </p> <p><strong>Annotations</strong></p> <p>Each of the audio patterns has an associated syllable level transcription of the audio pattern. The transcription is obtained from the score for the pattern and is not time aligned to the audio. The transcription is done using a reduced set of five syllables and is sufficient to computationally model the timbres of all the syllables. The annotations are stored as Hidden Markov Model Toolkit (HTK) label files. There is also a single master label file provided for batch processing using HTK (<a href="http://htk.eng.cam.ac.uk/">http://htk.eng.cam.ac.uk/</a>). </p> <p><strong>Dataset organization</strong></p> <p>The dataset has wav files and label files. The files are named as</p> <pre><code><pID><InstID>.<extension></code></pre> <p>The pID is as in Table 1, instID is a three digit identifier for the specific instance of the pattern, and extension can be .wav for the audio file or .lab for the label file. pID ϵ {10, 11, 12, 13, 14}, InstID ϵ {1, 2, ..., N<sub>pID</sub>}. e.g. The audio file and the label file for the fifth instance of the pattern duotuo is named 12005.wav and 12005.lab, respectively. The master label file is called masterLabels.lab</p> <p><strong>Using this dataset</strong></p> <p>If you use the dataset in your work, please cite the following publication:</p> <blockquote> <p>Ajay Srinivasamurthy, Rafael Caro Repetto, Harshavardhan Sundar, Xavier Serra, "Transcription and Recognition of Syllable based Percussion Patterns: The Case of Beijing Opera," in Proceedings of the 15th International Society for Music Information Retrieval (ISMIR) Conference, Taipei, Taiwan, Oct 2014.</p> </blockquote> <p><a href="http://hdl.handle.net/10230/25677">http://hdl.handle.net/10230/25677</a></p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p><strong>CONTACT</strong></p> <p>If you have any questions or comments about the dataset, please feel free to write to us.</p> <p>Ajay Srinivasamurthy (ajays.murthy@upf.edu)</p> <p>Rafael Caro Repetto (rafael.caro@upf.edu)</p> <p> </p> <p><a href="http://compmusic.upf.edu/bopp-dataset">http://compmusic.upf.edu/bopp-dataset</a></p>
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