The Topology of Music Recommendation Networks

We study the topology of several music recommendation networks, which rise from relationships between artist, co-occurrence of songs in playlists or experts' recommendation. The analysis uncovers the emergence of complex network phenomena in this kind of recommendation networks, built considering artists as nodes and their resemblance as links. We observe structural properties that provide some hints on navigation and possible optimizations on the design of music recommendation systems. Finally, the analysis derived from existing music knowledge sources provides a deeper understanding of the human music similarity perceptions.Comment: 15 pages, 3 figure

Generating Preview Tables for Entity Graphs

Users are tapping into massive, heterogeneous entity graphs for many applications. It is challenging to select entity graphs for a particular need, given abundant datasets from many sources and the oftentimes scarce information for them. We propose methods to produce preview tables for compact presentation of important entity types and relationships in entity graphs. The preview tables assist users in attaining a quick and rough preview of the data. They can be shown in a limited display space for a user to browse and explore, before she decides to spend time and resources to fetch and investigate the complete dataset. We formulate several optimization problems that look for previews with the highest scores according to intuitive goodness measures, under various constraints on preview size and distance between preview tables. The optimization problem under distance constraint is NP-hard. We design a dynamic-programming algorithm and an Apriori-style algorithm for finding optimal previews. Results from experiments, comparison with related work and user studies demonstrated the scoring measures' accuracy and the discovery algorithms' efficiency.Comment: This is the camera-ready version of a SIGMOD16 paper. There might be tiny differences in layout, spacing and linebreaking, compared with the version in the SIGMOD16 proceedings, since we must submit TeX files and use arXiv to compile the file

Audio Source Separation Using Sparse Representations

This is the author's final version of the article, first published as A. Nesbit, M. G. Jafari, E. Vincent and M. D. Plumbley. Audio Source Separation Using Sparse Representations. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 10, pp. 246-264. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch010file: NesbitJafariVincentP11-audio.pdf:n\NesbitJafariVincentP11-audio.pdf:PDF owner: markp timestamp: 2011.02.04file: NesbitJafariVincentP11-audio.pdf:n\NesbitJafariVincentP11-audio.pdf:PDF owner: markp timestamp: 2011.02.04The authors address the problem of audio source separation, namely, the recovery of audio signals from recordings of mixtures of those signals. The sparse component analysis framework is a powerful method for achieving this. Sparse orthogonal transforms, in which only few transform coefficients differ significantly from zero, are developed; once the signal has been transformed, energy is apportioned from each transform coefficient to each estimated source, and, finally, the signal is reconstructed using the inverse transform. The overriding aim of this chapter is to demonstrate how this framework, as exemplified here by two different decomposition methods which adapt to the signal to represent it sparsely, can be used to solve different problems in different mixing scenarios. To address the instantaneous (neither delays nor echoes) and underdetermined (more sources than mixtures) mixing model, a lapped orthogonal transform is adapted to the signal by selecting a basis from a library of predetermined bases. This method is highly related to the windowing methods used in the MPEG audio coding framework. In considering the anechoic (delays but no echoes) and determined (equal number of sources and mixtures) mixing case, a greedy adaptive transform is used based on orthogonal basis functions that are learned from the observed data, instead of being selected from a predetermined library of bases. This is found to encode the signal characteristics, by introducing a feedback system between the bases and the observed data. Experiments on mixtures of speech and music signals demonstrate that these methods give good signal approximations and separation performance, and indicate promising directions for future research

The PEP survey: clustering of infrared-selected galaxies and structure formation at z~2 in the GOODS South

ABRIDGED-This paper presents the first direct estimate of the 3D clustering properties of far-infrared sources up to z~3. This has been possible thanks to the Pacs Evolutionary Probe (PEP) survey of the GOODS South field performed with the PACS instrument onboard the Herschel Satellite. An analysis of the two-point correlation function over the whole redshift range spanned by the data reports for the correlation length, r_0~6.3 Mpc and r_0~6.7 Mpc, respectively at 100um and 160um, corresponding to dark matter halo masses M>~10^{12.4} M_sun. Objects at z~2 instead seem to be more strongly clustered, with r_0~19 Mpc and r_0~17 Mpc in the two considered PACS channels. This dramatic increase of the correlation length between z~1 and z~2 is connected with the presence of a wide, M>~10^{14} M_sun, filamentary structure which includes more than 50% of the sources detected at z~2. An investigation of the properties of such sources indicates the possibility for boosted star-forming activity in those which reside within the overdense environment with respect of more isolated galaxies found in the same redshift range. Lastly, we also present our results on the evolution of the relationship between luminous and dark matter in star-forming galaxies between z~1 and z~2. We find that the increase of (average) stellar mass in galaxies between z~1 and z~2 is about a factor 10 lower than that of the dark matter haloes hosting such objects ([z~1]/[z~2] ~ 0.4 vs M_{halo}[z~1]/M_{halo}[z~2] ~ 0.04). Our findings agree with the evolutionary picture of downsizing whereby massive galaxies at z~2 were more actively forming stars than their z~1 counterparts, while at the same time contained a lower fraction of their mass in the form of luminous matter.Comment: 14 pages, 8 figures, MNRAS accepte