Melodic Phrase Segmentation By Deep Neural Networks

Automated melodic phrase detection and segmentation is a classical task in content-based music information retrieval and also the key towards automated music structure analysis. However, traditional methods still cannot satisfy practical requirements. In this paper, we explore and adapt various neural network architectures to see if they can be generalized to work with the symbolic representation of music and produce satisfactory melodic phrase segmentation. The main issue of applying deep-learning methods to phrase detection is the sparse labeling problem of training sets. We proposed two tailored label engineering with corresponding training techniques for different neural networks in order to make decisions at a sequential level. Experiment results show that the CNN-CRF architecture performs the best, being able to offer finer segmentation and faster to train, while CNN, Bi-LSTM-CNN and Bi-LSTM-CRF are acceptable alternatives

Dual‐templating surface gel into thin SSZ‐13 zeolite membrane for fast selective hydrogen separation

Abstract: Highly permeable zeolite membranes are desirable for fast gas separation in the industry. Reducing the membrane's thickness is deemed to be an optimal solution for permeability improvement. Herein, we report the synthesis route of thin SSZ‐13 zeolite membranes via the conversion of template‐contained surface gels. The synthesis gel is fully crystallized into crack‐free SSZ‐13 membranes assisted with dual templates of N, N, N‐trimethyl‐1‐adamantammonium hydroxide (TMAdaOH) and tetraethylammonium hydroxide (TEAOH). The specific functions of TMAdaOH for structure directing and TEAOH for crystallization regulating are well discussed. Thin surface gel layer is impregnated onto porous alumina with subsequent crystallization into a 500 nm thick membrane. This submicron‐thick membrane exhibits high H2 permeance with 50 × 10−8 mol s−1 m−2 Pa−1 during hydrogen separation. Meanwhile, the separation factors are retained around 23.0 and 31.5 for H2/C2H6 and H2/C3H8, respectively. This approach offers a possibility for obtaining high‐quality zeolite membranes for efficient hydrogen separation

Limited angle tomography for transmission X-ray microscopy using deep learning

In transmission X-ray microscopy (TXM) systems, the rotation of a scanned sample might be restricted to a limited angular range to avoid collision with other system parts or high attenuation at certain tilting angles. Image reconstruction from such limited angle data suffers from artifacts because of missing data. In this work, deep learning is applied to limited angle reconstruction in TXMs for the first time. With the challenge to obtain sufficient real data for training, training a deep neural network from synthetic data is investigated. In particular, U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic ellipsoid data and multi-category data to reduce artifacts in filtered back-projection (FBP) reconstruction images. The proposed method is evaluated on synthetic data and real scanned chlorella data in 100° limited angle tomography. For synthetic test data, U-Net significantly reduces the root-mean-square error (RMSE) from 2.55 × 10−3 µm−1 in the FBP reconstruction to 1.21 × 10−3 µm−1 in the U-Net reconstruction and also improves the structural similarity (SSIM) index from 0.625 to 0.920. With penalized weighted least-square denoising of measured projections, the RMSE and SSIM are further improved to 1.16 × 10−3 µm−1 and 0.932, respectively. For real test data, the proposed method remarkably improves the 3D visualization of the subcellular structures in the chlorella cell, which indicates its important value for nanoscale imaging in biology, nanoscience and materials science