Affective Music Information Retrieval

Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio

Parental scaffolding behaviours during co-­viewing of television with their preschool children in Taiwan

The digital media play an increasingly pervasive and influential role in children’s lives (Rideout & VJR Consulting, 2011). However, whilst there has been extensive investigation into the media use of this age--‐group in the USA and western Europe, there has been little research on the media use of children under the age of 6 in Taiwan. Therefore, Phase 1 of the study began by conducting an online survey (n=535) in order to situate the work undertaken in Phase 2. The results showed that TV dominates the media use of young Taiwanese children. \ud Opinions differ regarding the effects of TV viewing on young children. Some child development specialists warn of the dangers of too much viewing, especially for infants (Christakis, 2008). However, more programmes are designed specifically for young children and many aim to support their learning. Evidence has shown that TV can have a positive impact on learning (Wright, Huston, Scantlin, & Kotler, 2001). The key issue is the extent to which children engage with the programme. The literature into children’s learning from media content indicates that the child’s engagement with the programme is strongly related to their understanding of the programme content (Calvert, Strong, Jacobs, & Conger, 2007). However, little is known about how parents can support their child’s engagement by co--‐viewing children’s TV programmes with them. Therefore, Phase 2 of the study aimed to explore in--‐depth this particular link between parental scaffolding and child engagement. Adopting a social constructive paradigm and using case study methodology, the researcher gathered video recordings of thirteen parent/child dyads of 3--‐ to 5--‐year--‐olds co--‐viewing the same episodes of two animated educational television programmes in natural conditions. In the analyses, measures of children’s engagement and thematic coding of the scaffolding behaviour of the parent were used to deductively and inductively analyse video recordings of the home observations. \ud The findings indicated that there is a positive association between the child’s engagement and the level of parental scaffolding. It is suggested that dissemination of the findings from this study could help parents to understand and appreciate the value of parent--‐child co--‐viewing of educational children’s television programmes and promote children’s learning from the programmes. \u

A mutant tat protein inhibits HIV-1 reverse transcription by targeting the reverse transcription complex

Previously, we reported that a mutant of Tat referred to as Nullbasic inhibits HIV-1 reverse transcription although the mechanism of action is unknown. Here we show that Nullbasic is a reverse transcriptase (RT) binding protein that targets the reverse transcription complex rather than directly inhibiting RT activity. An interaction between Nullbasic and RT was observed by using coimmunoprecipitation and pulldown assays, and a direct interaction was measured by using a biolayer interferometry assay. Mixtures of recombinant 6 x His-RT and Nullbasic-FLAG-V5-6 x His at molar ratios of up to 1:20,000 did not inhibit RT activity in standard homopolymer primer template assays. An analysis of virus made by cells that coexpressed Nullbasic showed that Nullbasic copurified with virus particles, indicating that it was a virion protein. In addition, analysis of reverse transcription complexes (RTCs) isolated from cells infected with wild type or Nullbasic-treated HIV-1 showed that Nullbasic reduced the levels of viral DNA in RTC fractions. In addition, a shift in the distribution of viral DNA and CAp24 to less-dense non-RTC fractions was observed, indicating that RTC activity from Nullbasic-treated virus was impaired. Further analysis showed that viral cores isolated from Nullbasic-treated HIV undergo increased disassembly in vitro compared to untreated HIV-1. To our knowledge, this is the first description of an antiviral protein that inhibits reverse transcription by targeting the RTC and affecting core stability

iTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data

BACKGROUND: Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) or microarray hybridization (ChIP-chip) has been widely used to determine the genomic occupation of transcription factors (TFs). We have previously developed a probabilistic method, called TIP (Target Identification from Profiles), to identify TF target genes using ChIP-seq/ChIP-chip data. To achieve high specificity, TIP applies a conservative method to estimate significance of target genes, with the trade-off being a relatively low sensitivity of target gene identification compared to other methods. Additionally, TIP’s output does not render binding-peak locations or intensity, information highly useful for visualization and general experimental biological use, while the variability of ChIP-seq/ChIP-chip file formats has made input into TIP more difficult than desired. DESCRIPTION: To improve upon these facets, here we present are fined TIP with key extensions. First, it implements a Gaussian mixture model for p-value estimation, increasing target gene identification sensitivity and more accurately capturing the shape of TF binding profile distributions. Second, it enables the incorporation of TF binding-peak data by identifying their locations in significant target gene promoter regions and quantifies their strengths. Finally, for full ease of implementation we have incorporated it into a web server (http://syslab3.nchu.edu.tw/iTAR/) that enables flexibility of input file format, can be used across multiple species and genome assembly versions, and is freely available for public use. The web server additionally performs GO enrichment analysis for the identified target genes to reveal the potential function of the corresponding TF. CONCLUSIONS: The iTAR web server provides a user-friendly interface and supports target gene identification in seven species, ranging from yeast to human. To facilitate investigating the quality of ChIP-seq/ChIP-chip data, the web server generates the chart of the characteristic binding profiles and the density plot of normalized regulatory scores. The iTAR web server is a useful tool in identifying TF target genes from ChIP-seq/ChIP-chip data and discovering biological insights. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2963-0) contains supplementary material, which is available to authorized users

Improving Automatic Jazz Melody Generation by Transfer Learning Techniques

In this paper, we tackle the problem of transfer learning for Jazz automatic generation. Jazz is one of representative types of music, but the lack of Jazz data in the MIDI format hinders the construction of a generative model for Jazz. Transfer learning is an approach aiming to solve the problem of data insufficiency, so as to transfer the common feature from one domain to another. In view of its success in other machine learning problems, we investigate whether, and how much, it can help improve automatic music generation for under-resourced musical genres. Specifically, we use a recurrent variational autoencoder as the generative model, and use a genre-unspecified dataset as the source dataset and a Jazz-only dataset as the target dataset. Two transfer learning methods are evaluated using six levels of source-to-target data ratios. The first method is to train the model on the source dataset, and then fine-tune the resulting model parameters on the target dataset. The second method is to train the model on both the source and target datasets at the same time, but add genre labels to the latent vectors and use a genre classifier to improve Jazz generation. The evaluation results show that the second method seems to perform better overall, but it cannot take full advantage of the genre-unspecified dataset.Comment: 8 pages, Accepted to APSIPA ASC(Asia-Pacific Signal and Information Processing Association Annual Summit and Conference ) 201

iTAR: A Web Server for Identifying Target Genes of Transcription Factors using ChIP-Seq or ChIP-Chip Data

Chromatin immunoprecipitation followed by massively parallel DNA sequencing (ChIP-seq) or microarray hybridization (ChIP-chip) has been widely used to determine the genomic occupation of transcription factors (TFs). We have previously developed a probabilistic method, called TIP (Target Identification from Profiles), to identify TF target genes using ChIP-seq/ChIP-chip data. To achieve high specificity, TIP applies a conservative method to estimate significance of target genes, with the trade-off being a relatively low sensitivity of target gene identification compared to other methods. Additionally, TIP’s output does not render binding-peak locations or intensity, information highly useful for visualization and general experimental biological use, while the variability of ChIP-seq/ChIP-chip file formats has made input into TIP more difficult than desired. To improve upon these facets, here we present are fined TIP with key extensions. First, it implements a Gaussian mixture model for p-value estimation, increasing target gene identification sensitivity and more accurately capturing the shape of TF binding profile distributions. Second, it enables the incorporation of TF binding-peak data by identifying their locations in significant target gene promoter regions and quantifies their strengths. Finally, for full ease of implementation we have incorporated it into a web server (http://syslab3.nchu.edu.tw/iTAR/) that enables flexibility of input file format, can be used across multiple species and genome assembly versions, and is freely available for public use. The web server additionally performs GO enrichment analysis for the identified target genes to reveal the potential function of the corresponding TF