We present a novel, innovative user interface to music repositories. Given an arbitrary collection of digital music files, our system creates a virtual landscape which allows the user to freely navigate in this collection. This is accomplished by automatically extracting features from the audio signal and training a Self-Organizing Map (SOM) on them to form clusters of similar sounding pieces of music. Subsequently, a Smoothed Data Histogram (SDH) is calculated on the SOM and interpreted as a three-dimensional height profile. This height profile is visualized as a three-dimensional island landscape containing the pieces of music. While moving through the terrain, the closest sounds with respect to the listener’s current position can be heard. This is realized by anisotropic auralization using a 5.1 surround sound model. Additionally, we incorporate knowledge extracted automatically from the web to enrich the landscape with semantic information. More precisely, we display words and related images that describe the heard music on the landscape to support the exploration.

An approach is presented to automatically build a search engine for large-scale music collections that can be queried through natural language. While existing approaches depend on explicit manual annotations and meta-data assigned to the individual audio pieces, we automatically derive descriptions by making use of methods from Web Retrieval and Music Information Retrieval. Based on the ID3 tags of a collection of mp3 files, we retrieve relevant Web pages via Google queries and use the contents of these pages to characterize the music pieces and represent them by term vectors. By incorporating complementary information about acoustic similarity we are able to both reduce the dimensionality of the vector space and improve the performance of retrieval, i.e. the quality of the results. Furthermore, the usage of audio similarity allows us to also characterize audio pieces when there is no associated information found on the Web.

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In this paper we present MusicRainbow which is a simple interface for discovering artists where colors encode different types of music. MusicRainbow is based on a new audiobased approach to compute artist similarity. This approach scores 15 percentage points higher in a genre classification task than the similarity computed on track level. Using a traveling salesman algorithm, similar artists are mapped near each other on a circular rainbow. Furthermore, we present a new approach of combining this audio-based information with information from the web. In particular, we label the rainbow and summarize the artists with words extracted from web pages related to the artists. We use different vocabularies for different hierarchical levels and heuristics to select the most descriptive labels. We conclude with a discussion of the results. The first impressions are very promising. 1.

Spatial layout is frequently used for managing loosely organized information, such as desktop icons and digital ink. To help users organize this type of information efficiently, we propose an interface for manipulating spatial aggregations of objects. The aggregated objects are automatically recognized as a group, and the group structure is visualized as a two-dimensional bubble surface that surrounds the objects. Users can drag, copy, or delete a group by operating on the bubble. Furthermore, to help pick out individual objects in a dense aggregation, the system spreads the objects to avoid overlapping when requested. This paper describes the design of this interface and its implementation. We tested our technique in icon grouping and ink relocation tasks and observed improvements in user performance. ACM Classification: H5.2 [Information interfaces and presentation]: User Interfaces – Graphical user interfaces.

This paper introduces our research aimed at building “active music listening interfaces”. This research approach is intended to enrich end-users’ music listening experiences by applying music-understanding technologies based on signal processing. Active music listening is a way of listening to music through active interactions. We have developed seven interfaces for active music listening, such as interfaces for skipping sections of no interest within a musical piece while viewing a graphical overview of the entire song structure, for displaying virtual dancers or song lyrics synchronized with the music, for changing the timbre of instrument sounds in compact-disc recordings, and for browsing a large music collection to encounter interesting musical pieces or artists. These interfaces demonstrate the importance of music-understanding technologies and the benefit they offer to end users. Our hope is that this work will help change music listening into a more active, immersive experience.

Portable digital music players are becoming pervasive and the size of personal digital music collections has been steadily increasing (5-10 thousand tracks are common today). The emerging area of Music Information Retrieval (MIR) deals with all aspects of managing, analyzing and organizing music in digital formats. The majority of work in MIR follows a search/retrieval paradigm. More recently, the importance of browsing as an interaction paradigm has been realized and several novel interfaces have been proposed. In this paper, we describe a tangible interface for content-aware browsing of music collections. The Radio Drum is a gestural interface based on capacitance sensors that can detect the x,y,z positions of two drum sticks in a 3D volume. We describe two possible mappings that can be be used for browsing music collections without relying on metadata. The first is an explicit mapping of tempo and beat strength, and the second is a music similarity space using audio feature extraction and a Self Organizing Map (SOM). 1.

Abstract. In this paper, we present an approach to automatically detecting music band members and instrumentation using web content mining techniques. To this end, we combine a named entity detection method with a rule-based linguistic text analysis approach extended by a rule filtering step. We report on the results of different evaluation experiments carried out on two test collections of bands covering a wide range of popularities. The performance of the proposed approach is evaluated using precision and recall measures. We further investigate the influence of different query schemes for the web page retrieval, of a critical parameter used in the rule filtering step, and of different string matching functions which are applied to deal with inconsistent spelling of band members. 1

This paper describes a novel audio only interface for selecting music which enables us to select songs without having to click a mouse. Using previous music players with normal headphones, we can hear only one song at a time and we thus have to play pieces individually to select the one we want to hear from numerous new music files, which involves a large number of mouse operations. The main advantage of our headphones is that they detect natural movements, such as the head or hand moving when users are listening to music and they can focus on a particular musical source that they want to hear. By moving their head left or right, listeners can hear the source from a frontal position as the digital compass detects the change in the direction they are facing. By looking up or down, the tilt sensor will detect the change in the face’s angle of elevation; they can better hear the source that is allocated to a more distant or closer position. By putting their hand behind their ear, listeners can adjust the focus sensor on the headphones to focus on a particular musical source that they want to hear.

This paper presents SongExplorer, a system for the exploration of large music collections on tabletop interfaces. SongExplorer addresses the problem of finding new interesting songs on large music databases, from an interaction design perspective. Using high level descriptors of musical songs, SongExplorer creates a coherent 2D map based on similarity, in which neighboring songs tend to be more similar. All songs are represented as throbbing circles that highlight their more relevant high-level properties, and the resulting music map is browseable and zoomable by the users who can use their fingers as well as specially designed tangible pucks, for helping them to find interesting music, independently of their previous knowledge of the collection. SongExplorer also offers basic player capabilities, allowing the users to organize the songs they have just discovered into playlists which can be manipulated as well as played and displayed. In this paper, the system hardware, software and interaction design are explained, and the usability tests carried are presented. Finally, conclusions and future work are discussed. 1.