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README.md
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## Dataset Description
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### Dataset Summary
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The integrated version provides the original content and the spectrogram generated in the experimental part of the paper cited above. For the second part, the pre-process in the paper is replicated. Each audio clip is a 3-second segment sampled at 44,100Hz, which is subsequently converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with the dimension of 88
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### Supported Tasks and Leaderboards
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MIR, audio classification
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## Dataset Description
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### Dataset Summary
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The integrated version provides the original content and the spectrogram generated in the experimental part of the paper cited above. For the second part, the pre-process in the paper is replicated. Each audio clip is a 3-second segment sampled at 44,100Hz, which is subsequently converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with the dimension of 88 x 258 x 1, representing the frequency-time structure of the audio. The label, on the other hand, is a 2-dimensional array with dimensions of 7 x 258, which indicates the presence of seven distinct techniques across each time frame. indicating the existence of the seven techniques in each time frame. In the end, given that the raw dataset has already been split into train, valid, and test sets, the integrated dataset maintains the same split method. This dataset can be used for frame-level guzheng playing technique detection.
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### Supported Tasks and Leaderboards
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MIR, audio classification
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