this paper, we examine both acoustic and subjective approaches for calculating similarity between artists, comparing their performance on a common database of 400 popular artists. Specifically, we evaluate acoustic techniques based on Mel-frequency cepstral coefficients and an intermediate `anchor space' of genre classification, and subjective techniques which use data from The All Music Guide, from a survey, from playlists and personal collections, and from web-text mining

Manifold approaches exist for organization of music by genre and/or style. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked by search engines to describe artists in terms of word occurrences on related pages. To classify artists we primarily use support vector machines. We present 3 experiments in which we address the following issues. First, we study the performance of our approach compared to previous work. Second, we investigate how daily fluctuations in the Internet affect our approach. Third, on a set of 224 artists from 14 genres we study (a) how many artists are necessary to define the concept of a genre, (b) which search engines perform best, (c) how to formulate search queries best, (d) which overall performance we can expect for classification, and finally (e) how our approach is suited as a similarity measure for artists.

This paper describes a method of mapping music into a semantic space that can be used for similarity measurement, classification, and music information retrieval. The value along each dimension of this anchor space is computed as the output from a pattern classifier which is trained to measure a particular semantic feature. In anchor space, distributions that represent objects such as artists or songs are modeled with Gaussian Mixture Models, and several similarity measures are defined by computing approximations to the Kullback-Leibler divergence between distributions. Similarity measures are evaluated against human similarity judgements. The models are also used for artist classification to achieve 62 % accuracy on a 25-artist set, and 38% on a 404-artist set (random guessing achieves 0.25%). Finally, we describe a music similarity browsing application that makes use of the fact that anchor space dimensions are meaningful to users. 1.

We describe the MusicMiner system for organizing large collections of music with databionic mining techniques. Low level audio features are extracted from the raw audio data on short time windows during which the sound is assumed to be stationary. Static and temporal statistics were consistently and systematically used for aggregation of low level features to form high level features. A supervised feature selection targeted to model perceptual distance between different sounding music lead to a small set of non-redundant sound features. Clustering and visualization based on these feature vectors can discover emergent structures in collections of music. Visualization based on Emergent Self-Organizing Maps in particular enables the unsupervised discovery of timbrally consistent clusters that may or may not correspond to musical genres and artists. We demonstrate the visualizations capabilities of the U-Map, displaying local sound differences based on the new audio features. An intuitive browsing of large music collections is offered based on the paradigm of topographic maps. The user can navigate the sound space and interact with the maps to play music or show the context of a song.

In this paper, we present a similarity measure for music artists based on search results of Google queries. Co-occurrences of artist names on web pages are analyzed to measure how often two artists are mentioned together on the same web page. We estimate conditional probabilities using the extracted page count. These conditional probabilities give a similarity measure which is evaluated using a data set containing 224 artists from 14 genres. For evaluation, we use two different methods, intra-/intergroup-similarities and k-Nearest Neighbors classification. Furthermore, a confidence filter and combinations of the results gained from three different query settings are tested. It is shown that these enhancements can raise the performance of our similarity measure. Comparing our results to those of similar approaches show that our approach, though being quite simple, performs well and can be used as a similarity measure that incorporates “social knowledge”.

Abstract. As digital music collections grow, so does the need to organizing them automatically. In this paper we present an approach to hierarchically organize music collections at the artist level. Artists are grouped according to similarity which is computed using a web search engine and standard text retrieval techniques. The groups are described by words found on the webpages using term selection techniques and domain knowledge. We compare different term selection techniques, present a simple demonstration, and discuss our findings. 1

A software system that automatically classifies MIDI files into hierarchically organized taxonomies of musical genres is presented. This extensible software includes an easy to use and flexible GUI. An extensive library of high-level musical features is compiled, including many original features. A novel hybrid classification system is used that makes use of hierarchical, flat and round robin classification. Both k-nearest neighbour and neural network-based classifiers are used, and feature selection and weighting are performed using genetic algorithms. A thorough review of previous research in automatic genre classification is presented, along with an overview of automatic feature selection and classification techniques. Also included is a discussion of the theoretical issues relating to musical genre, including but not limited to what mechanisms humans use to classify music by genre and how realistic genre taxonomies can be constructed.

Detecting artists that can be considered as prototypes for particular genres or styles of music is an interesting task. In this paper, we present an approach that ranks artists according to their prototypicality. To calculate such a ranking, we use asymmetric similarity matrices obtained via co-occurrence analysis of artist names on web pages. We demonstrate our approach on a data set containing 224 artists from 14 genres and evaluate the results using the rank correlation between the prototypicality ranking and a ranking obtained by page counts of search queries to Google that contain artist and genre. High positive rank correlations are achieved for nearly all genres of the data set. Furthermore, we elaborate a visualization method that illustrates similarities between artists using the prototypes of all genres as reference points. On the whole, we show how to create a prototypicality ranking and use it, together with a similarity matrix, to visualize a music repository.

We explore a simple, web-based method for predicting the genre of a given artist based on co-occurrence analysis, i.e. analyzing co-occurrences of artist and genre names on music-related web pages. To this end, we use the page counts provided by Google to estimate the relatedness of an arbitrary artist to each of a set of genres. We investigate four different query schemes for obtaining the page counts and two different probabilistic approaches for predicting the genre of a given artist. Evaluation is performed on two test collections, a large one with a quite general genre taxonomy and a quite small one with rather specific genres. Since our approach yields estimates for the relatedness of an artist to every genre of a given genre set, we can derive genre distributions which incorporate information about artists that cannot be assigned a single genre. This allows us to overcome the inflexible artist-genre assignment usually used in music information systems. We present a simple method to visualize such genre distributions with our Traveller’s Sound Player. Finally, we briefly outline how to adapt the presented approach to extract other properties of music artists from the web.

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web 2.0 ” recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of 360 classifiers trained using the online ensemble learning algorithm FilterBoost, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the “cold-start problem ” common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system. Because the words we learn are the same as those used by people who label their music collections, it is easy to integrate our predictions into existing similarity and prediction methods based on web data. 1