A Combinatorial Approach to Content-Based Music Selection

1999

Advances in networking and transmission of digital multimedia data bring huge catalogues of multimedia items to users. In the case of music, accessing these catalogues raises a problem for users and content providers, which we define as the music selection problem. From the user point of view, the goals are to match preferences, as well as provide them with new music. From the content provider viewpoint, the goal is to exploit the catalogue in an optimal fashion. We propose a novel approach to music selection, based on computing coherent sequences of music titles, and show that this amounts to solving a combinatorial pattern generation problem. We propose a language to specify these sequences and a solving technique based on global constraints.

2002

The issue of generating automatically sequences of music titles that satisfy arbitrary criteria such as user preferences has gained interest recently, because of the numerous applications in the field of Electronic Music Distribution. All the approaches proposed so far suffer from two main drawbacks: reduced expressiveness and incapacity to handle large music catalogues. We present in this paper a system that is able to produce automatically music playlists out of large, real catalogues (up to 200,000 titles), and that can handle arbitrarily complex criteria. We describe in this paper the basic algorithm and its adaptation to playlist generation, and report on experiments performed in the context of the IST European project Cuidado.

2007

ABSTRACT This paper proposes content-based music information retrieval (MIR) methods based on user preferences, which aim to improve the accuracy of MIR for users with “diverse” preferences, ie, users whose preferences range in songs with a wide variety of features. The proposed MIR method dynamically generates an optimal set of query vectors from the sample set of songs submitted by the user to express their preferences, based on the similarity of the songs in the sample set.

2006

We present an algorithm for use in an interactive music system that automatically generates music playlists that fit the music preferences given by a user. To this end, we introduce a formal model, define the problem of automatic playlist generation (APG) and indicate its NP-hardness. We use a local search (LS) procedure based on simulated annealing (SA) to solve the APG problem. In order to employ this LS procedure, we introduce an optimization variant of the APG problem, which includes the definition of penalty functions and a neighborhood structure. To improve upon the performance of the standard SA algorithm, we incorporated three heuristics referred to as song domain reduction, partial constraint voting, and two-level neighborhood structure. In tests, LS performed better than a constraint satisfaction (CS) solution in terms of run time, scalability and playlist quality.

2004

MiDiLiB is a six year research project on digital music libraries funded by the German Research Foundation (DFG) as a part of the Distributed Processing and Delivery of Digital Documents (V 3 D 2 ) research initiative. MiDiLiB's main focus is the development of contentbased retrieval algorithms for both score-and waveform-based music. In this paper we give an overview of our research results, describe several prototypical systems for content-based music retrieval which have been developed during the project, and discuss applications of the presented techniques in the context of today's and future digital music libraries.

Proceedings of the first ACM international conference on Digital libraries - DL '96, 1996

Music is traditionally retrieved by title, composer or subject classification. It is possible, with current technology, to retrieve music from a database on the basis of a few notes sung or hummed into a microphone. This paper describes the implementation of such a system, and discusses several issues pertaining to music retrieval. We first describe an interface that transcribes acoustic input into standard music notation. We then analyze string matching requirements for ranked retrieval of music and present the results of an experiment which tests how accurately people sing well known melodies. The performance of several string matching criteria are analyzed using two folk song databases. Finally, we describe a prototype system which has been developed for retrieval of tunes from acoustic input.

Big Data and Cognitive Computing

We address the problem of scalable content-based search in large collections of music documents. Music content is highly complex and versatile and presents multiple facets that can be considered independently or in combination. Moreover, music documents can be digitally encoded in many ways. We propose a general framework for building a scalable search engine, based on (i) a music description language that represents music content independently from a specific encoding, (ii) an extendible list of feature-extraction functions, and (iii) indexing, searching, and ranking procedures designed to be integrated into the standard architecture of a text-oriented search engine. As a proof of concept, we also detail an actual implementation of the framework for searching in large collections of XML-encoded music scores, based on the popular ElasticSearch system. It is released as open-source in GitHub, and available as a ready-to-use Docker image for communities that manage large collections o...

Proceedings of the fourth ACM conference on Digital libraries - DL '99, 1999

Digital libraries of music have the potential to capture popular imagination in ways that more scholarly libraries cannot. We are working towards a comprehensive digital library of musical material, including popular music. We have developed new ways of collecting musical material, accessing it through searching and browsing, and presenting the results to the user. We work with different representations of music: facsimile images of scores, the internal representation of a music editing program, page images typeset by a music editor, MIDI files, audio files representing sung user input, and textual metadata such as title, composer and arranger, and lyrics. This paper describes a comprehensive suite of tools that we have built for this project. These tools gather musical material, convert between many of these representations, allow searching based on combined musical and textual criteria, and help present the results of searching and browsing. Although we do not yet have a single fully-blown digital music library, we have built several exploratory prototype collections of music, some of them very large (100,000 tunes), and critical components of the system have been evaluated.

Content-Based Multimedia …, 2011

The amount of digital music has grown unprecedentedly during the last years and requires the development of effective methods for search and retrieval. In particular, content-based preference elicitation for music recommendation is a challenging problem that is effectively addressed in this paper. We present a system which automatically generates recommendations and visualizes a user's musical preferences, given her/his accounts on popular online music services. Using these services, the system retrieves a set of tracks preferred by a user, and further computes a semantic description of musical preferences based on raw audio information. For the audio analysis we used the capabilities of the Canoris API. Thereafter, the system generates music recommendations, using a semantic music similarity measure, and a user's preference visualization, mapping semantic descriptors to visual elements.

wwwiti.cs.uni-magdeburg.de

Keeping one's personal music collections well organized can be a very tedious task. Fortunately, today, many popular music players (such as AmaroK or iTunes) have an integrated library function that can automatically rename and tag music files and sort them into subdirectories. However, their common approach to stick with some hierarchy of genre, artist name, and album title barely represents the way a user would structure his collection manually. When it comes to organizing a music collection according to a user-specific hierarchy, three things are required: First, the music files have to be described by appropriate features beyond simple meta-tags. This includes content-based analysis but also incorporation of external information sources such as the web. Second, knowledge about the user's structuring preferences must be available. And third, and most importantly, methods for learning personalized hierarchies that can integrate this knowledge are needed. We propose for this task a hierarchical constraint based clustering approach that can weight the importance of different features according to the user perceived similarity. A hierarchy that is built based on this similarity measure reflects a user's view on the collection.