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

A DFT-Based Running Time Prediction Algorithm for Web Queries

Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the topk documents that best match each incoming query by means of a document ranking operation. To achieve high performance, dynamic pruning techniques such as the WAND and BM-WAND algorithms are used to avoid fully processing all of the documents related to a query during the ranking operation. Additionally, the index service distributes the ranking operations among clusters of processors wherein in each processor multi-threading is applied to speed up query solution. In this scenario, a query running time prediction algorithm has practical applications in the efficient assignment of processors and threads to incoming queries. We propose a prediction algorithm for the WAND and BM-WAND algorithms. We experimentally show that our proposal is able to achieve accurate prediction results while significantly reducing execution time and memory consumption as compared against an alternative prediction algorithm. Our proposal applies the discrete Fourier transform (DFT) to represent key features affecting query running time whereas the resulting vectors are used to train a feed-forward neural network with back-propagation.Fil: Rojas, Oscar. Universidad de Santiago de Chile; ChileFil: Gil Costa, Graciela Verónica. Universidad Nacional de San Luis. Facultad de Ciencias Físico- Matemáticas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; ArgentinaFil: Marín, Mauricio. Universidad de Chile; Chil

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Affect-based indexing and retrieval of multimedia data

Digital multimedia systems are creating many new opportunities for rapid access to content archives. In order to explore these collections using search, the content must be annotated with significant features. An important and often overlooked aspect o f human interpretation o f multimedia data is the affective dimension. The hypothesis o f this thesis is that affective labels o f content can be extracted automatically from within multimedia data streams, and that these can then be used for content-based retrieval and browsing. A novel system is presented for extracting affective features from video content and mapping it onto a set o f keywords with predetermined emotional interpretations. These labels are then used to demonstrate affect-based retrieval on a range o f feature films. Because o f the subjective nature o f the words people use to describe emotions, an approach towards an open vocabulary query system utilizing the electronic lexical database WordNet is also presented. This gives flexibility for search queries to be extended to include keywords without predetermined emotional interpretations using a word-similarity measure. The thesis presents the framework and design for the affectbased indexing and retrieval system along with experiments, analysis, and conclusions

Using Search Term Positions for Determining Document Relevance

The technological advancements in computer networks and the substantial reduction of their production costs have caused a massive explosion of digitally stored information. In particular, textual information is becoming increasingly available in electronic form. Finding text documents dealing with a certain topic is not a simple task. Users need tools to sift through non-relevant information and retrieve only pieces of information relevant to their needs. The traditional methods of information retrieval (IR) based on search term frequency have somehow reached their limitations, and novel ranking methods based on hyperlink information are not applicable to unlinked documents. The retrieval of documents based on the positions of search terms in a document has the potential of yielding improvements, because other terms in the environment where a search term appears (i.e. the neighborhood) are considered. That is to say, the grammatical type, position and frequency of other words help to clarify and specify the meaning of a given search term. However, the required additional analysis task makes position-based methods slower than methods based on term frequency and requires more storage to save the positions of terms. These drawbacks directly affect the performance of the most user critical phase of the retrieval process, namely query evaluation time, which explains the scarce use of positional information in contemporary retrieval systems. This thesis explores the possibility of extending traditional information retrieval systems with positional information in an efficient manner that permits us to optimize the retrieval performance by handling term positions at query evaluation time. To achieve this task, several abstract representation of term positions to efficiently store and operate on term positional data are investigated. In the Gauss model, descriptive statistics methods are used to estimate term positional information, because they minimize outliers and irregularities in the data. The Fourier model is based on Fourier series to represent positional information. In the Hilbert model, functional analysis methods are used to provide reliable term position estimations and simple mathematical operators to handle positional data. The proposed models are experimentally evaluated using standard resources of the IR research community (Text Retrieval Conference). All experiments demonstrate that the use of positional information can enhance the quality of search results. The suggested models outperform state-of-the-art retrieval utilities. The term position models open new possibilities to analyze and handle textual data. For instance, document clustering and compression of positional data based on these models could be interesting topics to be considered in future research