KEY BENEFIT: Written by a leader in the field of information retrieval, this text provides the background and tools needed to evaluate, compare and modify search engines. KEY TOPICS: Coverage of the underlying IR and mathematical models reinforce key concepts. Numerous programming exercises make extensive use of Galago, a Java-based open source search engine. MARKET: A valuable tool for search engine and information retrieval professionals.

Search Engines: Information Retrieval in Practice

Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.

A Survey of Audio-Based Music Classification and Annotation

Les approches utilisees pour la recherche d'information musicale (RIM) sont multidisciplinaires : bibliotheconomie et science de l'information, musicologie, theorie musicale, ingenierie du son, informatique, droit et commerce... L'article vise a identifier et a expliquer la problematique de de la RIM alors qu'elle devient une discipline a part entiere, les influences historiques, l'etat-de-l'art de la recherche et les solutions potentielles. L'information musicale est multifacette - ton, temporalite, harmonie, timbre, edition, texte et bibliographie - , l'acces a chacune de ces facettes constituant un defi pour la recherche et le developpement. Mais la RIM represente egalement un defi multirepresentationnel, multiculturel, multiexperience et multidisciplinaire. Les systemes de RIM deploient differents degres d'exhaustivite representationnelle. Ils relevent generalement de deux types : les systemes analytiques ou de production, et les systemes de localisation. En conclusion, l'A. mentionne quelques ateliers et symposiums recents ainsi que les principaux projets de recherche concernant la RIM.

Music Information Retrieval.

Comunicacio presentada a la 14th International Society for Music Information Retrieval Conference, celebrada a Curitiba (Brasil) els dies 4 a 8 de novembre de 2013.

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Essentia: An Audio Analysis Library for Music Information Retrieval.

We present an algorithm that uses finite automata to find the common motifs with gaps occurring in all strings belonging to a finite set S = {S1,S2,...,Sr}. In order to find these common motifs we must first identify the factors that exist in each string. Therefore the algorithm begins by constructing a factor automaton for each string Si. To find the common factors of all the strings, the algorithm needs to gather all the factors from the strings together in one data structure and this is achieved by computing an automaton that accepts the union of the above-mentioned automata. Using this automaton we are able to create a new factor alphabet. Based on this factor alphabet a finite automaton is created for each string Si that accepts sequences of all non overlapping factors residing in each string. The intersection of the latter automata produces the finite automaton which accepts all the common subsequences with gaps over the factor alphabet that are present in all the strings of the set S = {S1,S2,...,Sr}. These common subsequences are the common motifs of the strings.

Finding common motifs with gaps using finite automata

String Matching Techniques for Musical Similarity and Melodic Recognition

Although a substantial number of research projects have addressed music information retrieval over the past three decades, the field is still very immature. Few of these projects involve complex (polyphonic) music; methods for evaluation are at a very primitive stage of development; none of the projects tackles the problem of realistically large-scale databases. Many problems to be faced are due to the nature of music itself. Among these are issues in human perception and cognition of music, especially as they concern the recognizability of a musical phrase. This paper considers some of the most fundamental problems in music information retrieval, challenging the common assumption that searching on pitch (or pitch-contour) alone is likely to be satisfactory for all purposes. This assumption may indeed be true for most monophonic (single-voice) music, but it is certainly inadequate for polyphonic (multi-voice) music. Even in the monophonic case it can lead to misleading results. The fact, long recognized in projects involving monophonic music, that a recognizable passage is usually not identical with the search pattern means that approximate matching is almost always necessary, yet this too is severely complicated by the demands of polyphonic music. Almost all text-IR methods rely on identifying approximate units of meaning, that is, words. A fundamental problem in music IR is that locating such units is extremely difficult, perhaps impossible.

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Problems of music information retrieval in the real world

This paper extends the familiar “query by humming” music retrieval framework into the polyphonic realm. As humming in multiple voices is quite difficult, the task is more accurately described as “query by audio example,” onto a collection of scores. To our knowledge, we are the first to use polyphonic audio queries to retrieve from polyphonic symbolic collections. Furthermore, as our results will show, we will not only use an audio query to retrieve a known item symbolic piece, but we will use it to retrieve an entire set of real-world composed variations on that piece, also in the symbolic format. The harmonic modeling approach which forms the basis of this work is a new and valuable technique which has both wide applicability and future potential.

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Polyphonic Score Retrieval Using Polyphonic Audio Queries: A Harmonic Modeling Approach

The International Symposium on Music Information Retrieval (ISMIR) was born on 13 August 1999. This paper expresses the opinions of three of ISMIR’s founders as they reflect upon what has happened during its first decade. The paper provides the background context for the events that led to the establishment of ISMIR. We highlight the first ISMIR, held in Plymouth, MA in October of 2000, and use it to elucidate key trends that have influenced subsequent ISMIRs. Indicators of growth and success drawn from ISMIR publication data are presented. The role that the Music Information Retrieval Evaluation eXchange (MIREX) has played at ISMIR is examined. The factors contributing to ISMIR's growth and success are also enumerated. The paper concludes with a set of challenges and opportunities that the newly formed International Society for Music Information Retrieval should embrace to ensure the future vitality of the conference series and the ISMIR community.

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Ten years of ismir: Reflections on challenges and opportunities

In this paper a number of issues relating to theapplication of string processing techniques on musicalsequences are discussed. A brief survey of somemusical string processing algorithms is given and someissues of melodic representation, abstraction,segmentation and categorisation are presented. Thispaper is not intended to provide solutions tostring processing problems but rather tohighlight possible stumbling-block areas andraise awareness of primarily music‐elatedparticularities that can cause problems in matchingapplications.

/pdf/pattern-processing-in-melodic-sequences-challenges-caveats-3ggjtgwu5e.pdf

Tim Crawford

Papers

String Matching Techniques for Musical Similarity and Melodic Recognition

Problems of music information retrieval in the real world

Polyphonic Score Retrieval Using Polyphonic Audio Queries: A Harmonic Modeling Approach

Ten years of ismir: Reflections on challenges and opportunities

Pattern Processing in Melodic Sequences: Challenges, Caveats and Prospects