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

Audio-based music similarity measures can be used to automatically generate playlists or recommendations. In this paper the similarity measure that won the ISMIR’04 genre classification contest is reviewed. In addition, further improvements are presented. In particular, two new descriptors are presented and combined with two previously published similarity measures. The performance is evaluated in a series of experiments on four music collections. The evaluations are based on genre classification, assuming that very similar tracks belong to the same genre. On two collections the improvements lead to a substantial performance increase.

It would be interesting and valuable to devise an automatic measure of the similarity between two musicians based only on an analysis of their recordings. To develop such a measure, however, presupposes some ‘ground truth ’ training data describing the actual similarity between certain pairs of artists that constitute the desired output of the measure. Since artist similarity is wholly subjective, such data is not easily obtained. In this paper, we describe several attempts to construct a full matrix of similarity measures between a set of some 400 popular artists by regularizing limited subjective judgment data. We also detail our attempts to evaluate these measures by comparison with direct subjective similarity judgments collected via a webbased survey in April 2002. Overall, we find that subjective artist similarities are quite variable between users—casting doubt on the concept of a single ‘ground truth’. Our best measure, however, gives reasonable agreement with the subjective data, and forms a useable stand-in. In addition, our evaluation methodology may be useful for comparing other measures of artist similarity. 1.

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.

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.

Musical genres aid in the listening-and-retrieval (L&R) process by allowing a user or consumer a sense of reference. By organizing physical shelves in record stores by genres, shoppers can browse and discover new music by walking down an aisle. But the digitization of

Our approach to generate recommendations for similar artists follows a recent tradition of authors tackling the problem not with content-based audio analysis. Following this novel procedure we rely on the acquisition, filtering and condensing of unstructured text-based information that can be found in the web. The beauty of this approach lies in the possibility to access so-called cultural metadata that is the agglomeration of several independent-originally subjective- perspectives about music. 1.

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.

Machines have the power and potential to make expressive music on their own. This thesis aims to computationally model the process of creating music using experience from listening to examples. Our unbiased signal-based solution models the life cycle of listening, composing, and performing, turning the machine into an active musician, instead of simply an instrument. We accomplish this through an analysis-synthesis technique by combined perceptual and structural modeling of the musical surface, which leads to a minimal data representation. We introduce a music cognition framework that results from the interaction of psychoacoustically grounded causal listening, a time-lag embedded feature representation, and perceptual similarity clustering. Our bottom-up analysis intends to be generic and uniform by recursively revealing metrical hierarchies and structures of pitch, rhythm, and timbre. Training is suggested for top-down unbiased supervision, and is demonstrated with the prediction of downbeat. This

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”.