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Besides, we release the note object detection dataset we use to train MusicYOLO, which is placed in the images folder. We first generate the spectrogram images from the sight-singing audios in train&valid folder. Then we cut the spectrogram images into many slicel images along the time axis. Then we annotate the note object in each slice image with labelme software. We break up all the slice images and divide them into training set and validation set according to the ratio of 7:3, which are used for the training and validation of MusicYOLO.

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SSVD v2.0 contains a train-valid set of 67 sight-sining songs and a test set of 127 sight-singing songs. The training set and validation set are not strictly separated. They are placed together under the train &valid folder. Each sight-singing audio contains a (onset, offset, pitch) annotation. The annotation method is as follows:

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Sight-singing Talent is an online WeChat applet that has more than 600 long-term users and more than 60,000 effective Sight-singing samples.

SSVD v1.0 was distributed at https://github.com/itec-hust/Sight-Singing-Vocal-Data, which consists of note-score alignment annotations apart from transcription annotations. SSVD v2.0 only contains the transcription annotations. Compared with SSVD v1.0, SSVD 2.0 has the following differences:

Sight-singing Vocal Dataset (SSVD) dataset includes sight-singing songs for automatic singing transcription, recorded from the Sight-singing Talent WeChat applet. SSVD v2.0 (this repository) is used in our ICASSP2022 paper: MUSICYOLO: A SIGHT-SINGING ONSET/OFFSET DETECTION FRAMEWORK BASED ON OBJECT DETECTION INSTEAD OF SPECTRUM FRAMES.