Second screen applications are becoming key for broadcasters exploiting the convergence of TV and Internet. Authoring such applications however remains costly. In this paper, we present a second screen authoring application that leverages multimedia content analytics and social media monitoring. A back-office is dedicated to easy and fast content ingestion, segmentation, description and enrichment with links to entities and related content. From the back-end, broadcasters can push enriched content to front-end applications providing customers with highlights, entity and content links, overviews of social network, etc. The demonstration operates on political debates ingested during the 2017 French presidential election, enabling insights on the debates.

Music structure estimation has recently emerged as a central topic within the field of music information retrieval. Indeed, as music is a highly structured information stream, knowledge of how a music piece is organized represents a key challenge to enhance the management and exploitation of large music collections. This paper focuses on the benefits that can be expected from a regularity constraint on the structural segmentation of popular music pieces. Specifically, here, we study how a constraint that favors structural segments of comparable size provides a better conditioning of the boundary estimation process. First, we propose a formulation of the structural segmentation task as an optimization process, which separates the contribution from the audio features and the one from the constraint. We illustrate how the corresponding cost function can be minimized using a Viterbi algorithm. We present briefly its implementation and results in three systems designed for and submitted to the MIREX 2010, 2011, and 2012 evaluation campaigns. Then, we explore the benefits of the regularity constraint as an efficient mean for combining the outputs of a selection of systems presented at MIREX between 2010 and 2015, yielding a level of performance competitive to that of the state-of-the-art on the “MIREX10” dataset (100 J-Pop songs from the RWC database).

This article proposes to analyze the structural regularities from the audio and video streams of TV-programs and explore their potential for the classification of videos into program collections. Our approach is based on the spectral analysis of distance matrices representing the short-and long-term dependancies within the audio and visual modalities of a video. We propose to compare two videos by their respective spectral features. We appreciate the benefits brought by the two modalities on the performances in the context of a K-nearest neighbor classification, and we test our approach in the context of an unsupervised clustering algorithm. These evaluations are performed on two datasets of French and Italian TV programs.

Today, technologies for information storage and transmission allow the creation and development of huge databases of multimedia content. Tools are needed to facilitate their access and browsing. In this context, this article focuses on the segmentation of a particular category of multimedia content, audio-visual musical streams, into music pieces. This category includes concert audio-video recordings, and sequences of music videos such as the ones found in musical TV channels. Current approaches consist in supervised clustering in a few audio classes (music, speech, noise), and, to our knowledge, no consistent evaluation has been performed yet in the case of audio-visual musical streams. In this paper, we aim at estimating the temporal boundaries of music pieces relying on the assumed homogeneity of their musical and visual properties. We consider an unsupervised approach based on the generalized likelihood ratio to evaluate the presence of statistical breakdowns of MFCCs, Chroma vectors, dominant Hue and Lightness over time. An evaluation of this approach on 15 manually annotated concert streams shows the advantage of combining tonal content features to timbral ones, and a modest impact from the joint use of visual features in boundary estimation.