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The lack of labeled data is a major obstacle in many music information retrieval tasks such as melody extraction, where labeling is extremely laborious or costly. Semi-supervised learning (SSL) provides a solution to alleviate the issue by leveraging a large amount of unlabeled data. In this paper, we propose an SSL method using teacher-student models for vocal melody extraction. The teacher model is pre-trained with labeled data and guides the student model to make identical predictions given unlabeled input in a self-training setting. We examine three setups of teacher-student models with different data augmentation schemes and loss functions. Also, considering the scarcity of labeled data in the test phase, we artificially generate large-scale testing data with pitch labels from unlabeled data using an analysis-synthesis method. The results show that the SSL method significantly increases the performance against supervised learning only and the improvement depends on the teacher-student models, the size of unlabeled data, the number of self-training iterations, and other training details. We also find that it is essential to ensure that the unlabeled audio has vocal parts. Finally, we show that the proposed SSL method enables a baseline convolutional recurrent neural network model to achieve performance comparable to state-of-the-arts., Comment: 8 pages, 5 figures, accepted for the 21st International Society for Music Information Retrieval Conference (ISMIR 2020)

We study the role of cold gas in quenching star formation in the green valley by analyzing ALMA 12CO (1–0) observations of three galaxies with resolved optical spectroscopy from the MaNGA survey. We present resolution-matched maps of the star formation rate and molecular gas mass. These data are used to calculate the star formation efficiency (SFE) and gas fraction ( fgas) for these galaxies separately in the central “bulge” regions and outer disks. We find that, for the two galaxies whose global specific star formation rate (sSFR) deviates most from the star formation main sequence, the gas fraction in the bulges is significantly lower than that in their disks, supporting an “inside-out” model of galaxy quenching. For the two galaxies where SFE can be reliably determined in the central regions, the bulges and disks share similar SFEs. This suggests that a decline in fgas is the main driver of lowered sSFR in bulges compared to disks in green valley galaxies. Within the disks, there exist common correlations between the sSFR and SFE and between sSFR and fgas on kiloparsec scales—the local SFE or fgas in the disks declines with local sSFR. Our results support a picture in which the sSFR in bulges is primarily controlled by fgas, whereas both SFE and fgas play a role in lowering the sSFR in disks. A larger sample is required to confirm if the trend established in this work is representative of the green valley as a whole.

We study the role of cold gas in quenching star formation in the green valley by analysing ALMA $^{12}$CO (1-0) observations of three galaxies with resolved optical spectroscopy from the MaNGA survey. We present resolution-matched maps of the star formation rate and molecular gas mass. These data are used to calculate the star formation efficiency (SFE) and gas fraction ($f_{\rm~gas}$) for these galaxies separately in the central `bulge' regions and outer disks. We find that, for the two galaxies whose global specific star formation rate (sSFR) deviates most from the star formation main sequence, the gas fraction in the bulges is significantly lower than that in their disks, supporting an `inside-out' model of galaxy quenching. For the two galaxies where SFE can be reliably determined in the central regions, the bulges and disks share similar SFEs. This suggests that a decline in $f_{\rm~gas}$ is the main driver of lowered sSFR in bulges compared to disks in green valley galaxies. Within the disks, there exist common correlations between the sSFR and SFE and between sSFR and $f_{\rm~gas}$ on kpc scales -- the local SFE or $f_{\rm~gas}$ in the disks declines with local sSFR. Our results support a picture in which the sSFR in bulges is primarily controlled by $f_{\rm~gas}$, whereas both SFE and $f_{\rm~gas}$ play a role in lowering the sSFR in disks. A larger sample is required to confirm if the trend established in this work is representative of green valley as a whole., 10 pages, 5 figures, 1 table; ApJ accepted