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We present a study on using a Mnemonic Self-Organizing Map for clustering a very homogeneous collection of mu- sic. In particular, we create a map containing the complete works of Wolfgang Amadeus Mozart. We study and analyze the clustering capabilities of the SOM on this very focused collection. We furthermore present a web-based application for e...
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... our study, we mapped the music collection onto a Mnemonic SOM in the shape of the silhouette of its composer. As visualizations, the user can choose between three different variants: (1) the map with the image of Mozart as back- ground, as depicted in Figure 1; (2) only the shape of Mozart with the SDH visualization; and (3) a combination of both, a semi-transparent SDH on the image, as shown in Figure 2. Additionally, the user can choose to show the distribution of the classes the pieces of music belong to. ...
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Citations
... SOMs have been applied to organize sound and music collections by acoustic relations [Cosi94], [Feiten94], [Spevak01], [Rauber01], [Pampalk04], [Mörchen05]. The mere mapping of music has been enhanced by visual representations of music clusters of a certain style ("Islands of Music", [Rauber03]), mnemonic shapes ("Map of Mozart", [Mayer06]) and a fully interactive interface for exploration of music collections and creation of playlists through visual interaction ("PlaySOM", [Neumayer05]). Other variations apply SOMs to organize music at the artist level using artist information mined from the web [Knees04] or to enable live clustering of radio stations through analysis of the station's program content [Lidy06]. ...
Search has become an important and necessary component of many diverse ICT
applications. A large number of business and application areas depend on the efficiency and
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The objective of this document is to provide an overview of the business areas, the research
challenges and the socio-economic aspects related to “Search Computing”.
... This concept has also been applied for Music Retrieval to generate music maps, such as in the SOMeJB [2] system. Specific domain applications of music maps are for example the Map of Mozart [3], which organises the complete works of Mozart in an appealing manner, or the Radio SOM [4], illustrating musical profiles of radio stations. A comprehensive overview on music maps, with a special focus on the user interaction with them, can be found in [5]. ...
A wealth of different feature sets for analysing music has been proposed and employed in several different Music Infor-mation Retrieval applications. In many cases, the feature sets are compared with each other based on benchmarks in supervised machine learning, such as automatic genre classification. While this approach makes features comparable for specific tasks, it doesn't reveal much detail on the specific musical characteristics captured by the single feature sets. In this paper, we thus perform an analytic comparison of several different audio feature sets by means of Self-Organising Maps. They perform a projection from a high dimensional input space (the audio features) to a lower dimensional output space, often a two-dimensional map, while preserving the topological order of the input space. Comparing the stability of this projection allows to draw conclusions on the specific properties of the single feature sets.
... The algorithm was first introduced in [15] and enhanced later by the inclusion of psycho-acoustic models in [16]. The feature set has proven to be applicable to both classification of music into genres [7] and automatic clustering of music archives according to the perceived sound similarity [10]. Furthermore, the correspondence of the resulting organization with emotional interpretations of the sound in various regions of the map has been analyzed [1]. ...
Sound and, specifically, music is a medium that is used for a wide range of purposes in different situations in very differ- ent ways. Ways for music selection and consumption may range from completely passive, almost unnoticed perception of background sound environments to the very specific se- lection of a particular recording of a piece of music with a specific orchestra and conductor on a certain event. Differ- ent systems and interfaces exist for the broad range of needs in music consumption. Locating a particular recording is well supported by traditional search interfaces via metadata. Other interfaces support the creation of playlists via artist or album selection, up to more artistic installation of sound environments that users can navigate through. In this paper we will present a set of systems that support the creation of as well as the navigation in musical spaces, both in the real world as well as in virtual environments. We show some common principles and point out further directions for a more direct coupling of the various spaces and interaction methods.
Digital methods are increasingly applied to store, structure and analyse vast amounts of musical data. In this context, visualization plays a crucial role, as it assists musicologists and non‐expert users in data analysis and in gaining new knowledge. This survey focuses on this unique link between musicology and visualization. We classify 129 related works according to the visualized data types, and we analyse which visualization techniques were applied for certain research inquiries and to fulfill specific tasks. Next to scientific references, we take commercial music software and public websites into account, that contribute novel concepts of visualizing musicological data. We encounter different aspects of uncertainty as major problems when dealing with musicological data and show how occurring inconsistencies are processed and visually communicated. Drawing from our overview in the field, we identify open challenges for research on the interface of musicology and visualization to be tackled in the future. image
Sound and, specifically, music is a medium that is used for a wide range of purposes in different situations in very different
ways. Ways for music selection and consumption range from completely passive, almost unnoticed perception of background sound
environments to the very specific selection of a particular recording of a piece of music with a specific orchestra and conductor
at a certain event. Different systems and interfaces exist for the broad range of needs in music consumption. Locating a particular
recording is well supported by traditional search interfaces via metadata. Other interfaces support the automatic creation
of playlists via artist or album selection, up to more artistic installations of sound environments that users can navigate
through. In this paper we present a set of systems that support the creation of as well as the navigation in musical spaces,
both in the real world as well as in virtual environments. We show common principles and point out further directions for
a more direct coupling of the various spaces and interaction methods, creating ambient sound environments and providing organic
interaction with music for different purposes.
This paper describes Musicream, a novel music-listening interface that lets a user unexpectedly come across various musical pieces similar to those liked by the user. Most existing “query-by-example” interfaces are based on similaritybased searching, so they return the same results for the same query, meaning that a user of those systems always receives the same list of musical pieces sorted by similarity. Therefore, most existing systems do not provide a user an opportunity to encounter various unfamiliar musical pieces by chance. Musicream facilitates active, flexible, and unexpected encounters with musical pieces by providing four functions: the music-disc streaming function which creates a flow of many musical-piece entities (discs) from a large music collection, the similarity-based sticking function which allows a user to easily pick out and listen to similar pieces from the flow, the meta-playlist function which can generate a playlist of playlists (ordered lists of pieces), and the time-machine function which automatically records all Musicream activities and allows a user to visit and retrieve a past state as if using a time machine. In our experiments, these functions were used seamlessly to achieve active and creative querying and browsing of music collections, confirming the effectiveness of Musicream.
The steep rise in music downloading over CD sales has created a major shift in the music industry away from physical media formats and towards online products and services. Music is one of the most popular types of online information and there are now hundreds of music streaming and download services operating on the World-Wide Web. Some of the music collections available are approaching the scale of ten million tracks and this has posed a major challenge for searching, retrieving, and organizing music content. Research efforts in music information retrieval have involved experts from music perception, cognition, musicology, engineering, and computer science engaged in truly interdisciplinary activity that has resulted in many proposed algorithmic and methodological solutions to music search using content-based methods. This paper outlines the problems of content-based music information retrieval and explores the state-of-the-art methods using audio cues (e.g., query by humming, audio fingerprinting, content-based music retrieval) and other cues (e.g., music notation and symbolic representation), and identifies some of the major challenges for the coming years.
