Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval

Deep cross-modal learning has successfully demonstrated excellent performance in cross-modal multimedia retrieval, with the aim of learning joint representations between different data modalities. Unfortunately, little research focuses on cross-modal correlation learning where temporal structures of different data modalities such as audio and lyrics should be taken into account. Stemming from the characteristic of temporal structures of music in nature, we are motivated to learn the deep sequential correlation between audio and lyrics. In this work, we propose a deep cross-modal correlation learning architecture involving two-branch deep neural networks for audio modality and text modality (lyrics). Data in different modalities are converted to the same canonical space where inter modal canonical correlation analysis is utilized as an objective function to calculate the similarity of temporal structures. This is the first study that uses deep architectures for learning the temporal correlation between audio and lyrics. A pre-trained Doc2Vec model followed by fully-connected layers is used to represent lyrics. Two significant contributions are made in the audio branch, as follows: i) We propose an end-to-end network to learn cross-modal correlation between audio and lyrics, where feature extraction and correlation learning are simultaneously performed and joint representation is learned by considering temporal structures. ii) As for feature extraction, we further represent an audio signal by a short sequence of local summaries (VGG16 features) and apply a recurrent neural network to compute a compact feature that better learns temporal structures of music audio. Experimental results, using audio to retrieve lyrics or using lyrics to retrieve audio, verify the effectiveness of the proposed deep correlation learning architectures in cross-modal music retrieval

Numerical Studies of Curved-walled Micro Nozzle/Diffuser

[[abstract]]In this study, commercially available software CFD was adopted for analyzing the performance of straight-walled and curved-walled micro nozzle/diffuser. Such nozzle/diffuser was used in valve-less micro-pumps. This model tested different types of nozzle/diffuser and the results showed that the pressure loss coefficient for nozzle/diffuser decreases with the increase of Reynolds number. At the same Reynolds number, the pressure loss coefficient for nozzle is higher than that of the diffuser. At a given volumetric flow rate, the pressure loss coefficient and ratio of pressure loss coefficient for curved-walled nozzle/diffuser are slightly higher than that of the straight-walled nozzle/diffuser. In this study, the numerical data was found good agreement with previous analytic solution and experimental results.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20060118~20060121[[booktype]]紙本[[booktype]]電子版[[conferencelocation]]Zhuhai, Chin

Magic wavelengths for the 6s^2\,^1S_0-6s6p\,^3P_1^o transition in ytterbium atom

The static and dynamic electric-dipole polarizabilities of the 6s^2\,^1S_0 and 6s6p\,^3P_1^o states of Yb are calculated by using the relativistic ab initio method. Focusing on the red detuning region to the 6s^2\,^1S_0-6s6p\,^3P_1^o transition, we find two magic wavelengths at 1035.7(2) nm and 612.9(2) nm for the 6s^2\,^1S_0-6s6p\,^3P_1^o, M_J=0 transition and three magic wavelengthes at 1517.68(6) nm, 1036.0(3) nm and 858(12) nm for the 6s^2\,^1S_0-6s6p\,^3P_1^o, M_J=\pm1 transitions. Such magic wavelengths are of particular interest for attaining the state-insensitive cooling, trapping, and quantum manipulation of neutral Yb atom.Comment: 13 pages, 3 figure