Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years

Interactive Video Search

With an increasing amount of video data in our daily life, the need for content-based search in videos increases as well. Though a lot of research has been spent on video retrieval tools and methods which allow for automatic search in videos through content-based queries, still the performance of automatic video retrieval is far from optimal. In this tutorial we discussed (i) proposed solutions for improved video content navigation, (ii) typical interaction of content-based querying features, and (iii) advanced video content visualization methods. Moreover, we discussed interactive video search systems and ways to evaluate their performance

Vision systems with the human in the loop

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed

Combining textual and visual information processing for interactive video retrieval: SCHEMA's participation in TRECVID 2004

In this paper, the two different applications based on the Schema Reference System that were developed by the SCHEMA NoE for participation to the search task of TRECVID 2004 are illustrated. The first application, named ”Schema-Text”, is an interactive retrieval application that employs only textual information while the second one, named ”Schema-XM”, is an extension of the former, employing algorithms and methods for combining textual, visual and higher level information. Two runs for each application were submitted, I A 2 SCHEMA-Text 3, I A 2 SCHEMA-Text 4 for Schema-Text and I A 2 SCHEMA-XM 1, I A 2 SCHEMA-XM 2 for Schema-XM. The comparison of these two applications in terms of retrieval efficiency revealed that the combination of information from different data sources can provide higher efficiency for retrieval systems. Experimental testing additionally revealed that initially performing a text-based query and subsequently proceeding with visual similarity search using one of the returned relevant keyframes as an example image is a good scheme for combining visual and textual information

Challenges and opportunities of context-aware information access

Ubiquitous computing environments embedding a wide range of pervasive computing technologies provide a challenging and exciting new domain for information access. Individuals working in these environments are increasingly permanently connected to rich information resources. An appealing opportunity of these environments is the potential to deliver useful information to individuals either from their previous information experiences or external sources. This information should enrich their life experiences or make them more effective in their endeavours. Information access in ubiquitous computing environments can be made "context-aware" by exploiting the wide range context data available describing the environment, the searcher and the information itself. Realizing such a vision of reliable, timely and appropriate identification and delivery of information in this way poses numerous challenges. A central theme in achieving context-aware information access is the combination of information retrieval with multiple dimensions of available context data. Potential context data sources, include the user's current task, inputs from environmental and biometric sensors, associated with the user's current context, previous contexts, and document context, which can be exploited using a variety of technologies to create new and exciting possibilities for information access

Fusion of Multimodal Information in Music Content Analysis

Music is often processed through its acoustic realization. This is restrictive in the sense that music is clearly a highly multimodal concept where various types of heterogeneous information can be associated to a given piece of music (a musical score, musicians\u27 gestures, lyrics, user-generated metadata, etc.). This has recently led researchers to apprehend music through its various facets, giving rise to "multimodal music analysis" studies. This article gives a synthetic overview of methods that have been successfully employed in multimodal signal analysis. In particular, their use in music content processing is discussed in more details through five case studies that highlight different multimodal integration techniques. The case studies include an example of cross-modal correlation for music video analysis, an audiovisual drum transcription system, a description of the concept of informed source separation, a discussion of multimodal dance-scene analysis, and an example of user-interactive music analysis. In the light of these case studies, some perspectives of multimodality in music processing are finally suggested

Learning Multimodal Latent Attributes

Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning