As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this paper, we investigate new shape-based search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D

Existing audio tools handle the increasing amount of computer audio data inadequately. The typical tape-recorder paradigm for audio interfaces is inflexible and time consuming, especially for large data sets. On the other hand, completely automatic audio analysis and annotation is impossible using current techniques.

With Islands of Music we present a system which facilitates exploration of music libraries without requiring manual genre classification. Given pieces of music in raw audio format we estimate their perceived sound similarities based on psychoacoustic models. Subsequently, the pieces are organized on a 2-dimensional map so that similar pieces are located close to each other. A visualization using a metaphor of geographic maps provides an intuitive interface where islands resemble genres or styles of music. We demonstrate the approach using a collection of 359 pieces of music.

Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100+ recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.

With the advent of large musical archives the need to provide an organization of these archives becomes eminent. While artist-based organizations or title indexes may help in locating a specific piece of music, a more intuitive, genre-based organization is required to allow users to browse an archive and explore its contents. Yet, currently these organizations following musical styles have to be designed manually.

Automatic music classification is essential for implementing efficient music information retrieval systems; meanwhile, it may shed light on the process of human’s music perception. This paper describes our work on the classification of folk music from different countries based on their monophonic melodies using hidden Markov models. Music corpora of Irish, German and Austrian folk music in various symbolic formats were used as the data set. Different representations and HMM structures were tested and compared. The classification performances achieved 75%, 77 % and 66 % for 2-way classifications and 63 % for 3-way classification using 6-state left-right HMM with the interval representation in the experiment. This shows that the melodies of folk music do carry some statistical features to distinguish them. We expect that the result will improve if we use a more discriminable data set and the approach should be applicable to other music classification tasks and acoustic musical signals. Furthermore, the results suggest to us a new way to think about musical style similarity.

Indexing and content-based retrieval are necessary to handle the large amounts of audio and multimedia data that is becoming available on the web and elsewhere. Since manual indexing using existing audio editors is extremely time consuming a number of automatic content analysis systems have been proposed. Most of these systems rely on speech recognition techniques to create text indices. On the other hand, very few systems have been proposed for automatic indexing of music and general audio. Typically these systems rely on classification and similarity-retrieval techniques and work in restricted audio domains. A somewhat

future trends

With the rising popularity of digital music archives the need for new access methods such as interactive exploration or similarity-based search become significant. In this paper we present the PlaySOM, as well as the PocketSOMPlayer, two novel interfaces allowing to browse a music collection by navigating a map of clustered music tracks and to select regions of interest containing similar tracks for playing. The PlaySOM system is primarily designed to allow interaction via a large-screen device, whereas the PocketSOMPlayer is implemented for mobile devices, supporting both local as well as streamed audio replay. This approach offers content-based organization of music as an alternative to conventional navigation of audio archives, i.e. flat or hierarchical listings of music tracks that are sorted and filtered by meta information.

This paper presents a automatic music retrieval system inspired by Mr. Lintgen's approach, and is thus named ARTHUR in his honor. Like its namesake, ARTHUR retrieves audio on the basis of long-term structure, specifically the variation of soft and louder passages. The system thus works best on music that has substantial dynamic variation, such as works in the orchestral canon. Unfortunately, this technique is not robust for much popular music, which generally has much less dynamic range (a shortcoming shared with Mr. Lintgen)