Finding new music: a diary study of everyday encounters with novel songs

This paper explores how we, as individuals, purposefully or serendipitously encounter 'new music' (that is, music that we haven’t heard before) and relates these behaviours to music information retrieval activities such as music searching and music discovery via use of recommender systems. 41 participants participated in a three-day diary study, in which they recorded all incidents that brought them into contact with new music. The diaries were analyzed using a Grounded Theory approach. The results of this analysis are discussed with respect to location, time, and whether the music encounter was actively sought or occurred passively. Based on these results, we outline design implications for music information retrieval software, and suggest an extension of 'laid back' searching

Music Information Retrieval Meets Music Education

This paper addresses the use of Music Information Retrieval (MIR) techniques in music education and their integration in learning software. A general overview of systems that are either commercially available or in research stage is presented. Furthermore, three well-known MIR methods used in music learning systems and their state-of-the-art are described: music transcription, solo and accompaniment track creation, and generation of performance instructions. As a representative example of a music learning system developed within the MIR community, the Songs2See software is outlined. Finally, challenges and directions for future research are described

FROM MUSIC INFORMATION RETRIEVAL (MIR) TO INFORMATION RETRIEVAL FOR MUSIC (IRM)

This thesis reviews and discusses certain techniques from the domain of (Music) Information Retrieval, in particular some general data mining algorithms. It also describes their specific adaptations for use as building blocks in the CACE4 software application. The use of Augmented Transition Networks (ATN) from the field of (Music) Information Retrieval is, to a certain extent, adequate as long as one keeps the underlying tonal constraints and rules as a guide to understanding the structure one is looking for. However since a large proportion of algorithmic music, including music composed by the author, is atonal, tonal constraints and rules are of little use. Analysis methods from Hierarchical Clustering Techniques (HCT) such as k-means and Expectation-Maximisation (EM) facilitate other approaches and are better suited for finding (clustered) structures in large data sets. ART2 Neural Networks (Adaptive Resonance Theory) for example, can be used for analysing and categorising these data sets. Statistical tools such as histogram analysis, mean, variance as well as correlation calculations can provide information about connections between members in a data set. Altogether this provides a diverse palette of usable data analysis methods and strategies for creating algorithmic atonal music. Now acting as (software) strategy tools, their use is determined by the quality of their output within a musical context, as demonstrated when developed and programmed into the Computer Assisted Composition Environment: CACE4. Music Information Retrieval techniques are therefore inverted: their specific techniques and associated methods of Information Retrieval and general data mining are used to access the organisation and constraints of abstract (non-specific musical) data in order to use and transform it in a musical composition

A Primer on U.S. Intellectual Property Rights Applicable to Music Information Retrieval Systems

Digital technology has had a significant impact on the ways in which music information can be stored, transmitted, and used. Within the information sciences, music information retrieval has become an increasingly important and complex field. This brief article is addressed primarily to those involved in the design and implementation of systems for storing and retrieving digital files containing musical notation, recorded music, and relevant metadata – hereinafter referred to as a Music Information Retrieval System (“MIRS”). In particular, this group includes information specialists, software engineers, and the attorneys who advise them. Although peer-to-peer computer applications, such as Napster’s MusicShare or the Kazaa Media Desktop, can be conceived of as creating a MIRS, my analysis focuses on MIRS designed or operated by an entity that takes responsibility for choosing and controlling the music information stored in the system. Examples would include digital music collections stored in research libraries or on university intranets, as well as commercial systems with similar design features. This article describes how certain provisions of U. S. intellectual property law apply to MIRSs and mentions relevant law in the European Union for purposes of comparison. The focus is on U. S. copyright law, with very brief mention of U.S. patent and trade secret law. Additionally, the article mentions proposed legislation for database protection, which already exists in the European Union. Enactment of such legislation could be very significant for developers and operators of MIRS

A Primer on U.S. Intellectual Property Rights Applicable to Music Information Retrieval Systems

Digital technology has had a significant impact on the ways in which music information can be stored, transmitted, and used. Within the information sciences, music information retrieval has become an increasingly important and complex field. This brief article is addressed primarily to those involved in the design and implementation of systems for storing and retrieving digital files containing musical notation, recorded music, and relevant metadata – hereinafter referred to as a Music Information Retrieval System (“MIRS”). In particular, this group includes information specialists, software engineers, and the attorneys who advise them. Although peer-to-peer computer applications, such as Napster’s MusicShare or the Kazaa Media Desktop, can be conceived of as creating a MIRS, my analysis focuses on MIRS designed or operated by an entity that takes responsibility for choosing and controlling the music information stored in the system. Examples would include digital music collections stored in research libraries or on university intranets, as well as commercial systems with similar design features. This article describes how certain provisions of U. S. intellectual property law apply to MIRSs and mentions relevant law in the European Union for purposes of comparison. The focus is on U. S. copyright law, with very brief mention of U.S. patent and trade secret law. Additionally, the article mentions proposed legislation for database protection, which already exists in the European Union. Enactment of such legislation could be very significant for developers and operators of MIRS

A Content-Aware Interactive Explorer of Digital Music Collections: The Phonos Music Explorer

La tesi si propone di utilizzare le più recenti tecnologie del Music Information Retrieval (MIR) al fine di creare un esploratore interattivo di cataloghi musicali. Il software utilizza tecniche avanzate quali riduzione di dimensionalità  mediante FastMap, generazione e streaming over-the-network di contenuto audio, segmentazione e estrazione di descrittori da segnali audio. Inoltre, il software è in grado di adattare in real-time il proprio output sulla base di interazioni dell'utent

MUSIC, BRAIN PLASTICITY AND THE RESILIENCE: THE PILLARS OF NEW RECEPTIVE THERAPY

This paper describes a new type of receptive music therapy which aims to build the patients’ psychological resilience by increasing the levels of dopamine, serotonin and oxytocin in order to increase standard psychopharmacological treatment efficiency. Previous research concerning the musically induced production of the two neurotransmitters and a hormone is discussed and reviewed. Based upon the existent studies concerning the influence of music on dopamine, serotonin and oxytocin induction, a new design of specific music features for this purpose is proposed and elaborated upon. The music features are numerically described using Music Information Retrieval software in order to objectivise the otherwise intuitively chosen music elements such as event density (number of notes started in one second of time), tempo, harmonic rhythm (number of harmonies changes in one second), dynamics, key changes and roughness coefficient (level of sensory dissonance). Finally, the new concept of resilience enhancing therapy is proposed and defined using the music features described above

Identifikace cover verzí skladeb pomocí harmonických příznaků, modelu harmonie a harmonické složitosti

Title: Cover Song Identification using Music Harmony Features, Model and Complexity Analysis Author: Ladislav Maršík Department: Department of Software Engineering Supervisor: Prof. RNDr. Jaroslav Pokorný, CSc., Department of Software Engineering Abstract: Analysis of digital music and its retrieval based on the audio fe- atures is one of the popular topics within the music information retrieval (MIR) field. Every musical piece has its characteristic harmony structure, but harmony analysis is seldom used for retrieval. Retrieval systems that do not focus on similarities in harmony progressions may consider two versions of the same song different, even though they differ only in instrumentation or a singing voice. This thesis takes various paths in exploring, how music harmony can be used in MIR, and in particular, the cover song identification (CSI) task. We first create a music harmony model based on the knowledge of music theory. We define novel concepts: a harmonic complexity of a mu- sical piece, as well as the chord and chroma distance features. We show how these concepts can be used for retrieval, complexity analysis, and how they compare with the state-of-the-art of music harmony modeling. An extensive comparison of harmony features is then performed, using both the novel fe- atures and the...Title: Identifikace cover verzí skladeb pomocí harmonických příznaků, mo- delu harmonie a harmonické složitosti Author: Ladislav Maršík Department: Department of Software Engineering Supervisor: Prof. RNDr. Jaroslav Pokorný, CSc., Department of Software Engineering Abstract: Analýza digitálnej hudby a jej efektívne vyhl'adávanie založené na príznakoch je populárnou témou v rámci odvetvia music information re- trieval (MIR). Každé hudobné dielo má svoju charakteristickú harmonickú štruktúru, ale jej analýza sa používa na vyhl'adávanie len zriedka. Pokial' aplikácia neporovnáva akordický priebeh skladby, môže sa stat' že nesprávne vyhlási dve skladby za rôzne, aj ked' sa v skutočnosti odlišujú len zmenou nástrojov alebo speváka. V tejto práci ukážeme viaceré možnosti ako využit' hudobnú harmóniu pre MIR, a to hlavne pre detekciu cover verzií skladieb. Najprv sa venujeme vytvoreniu modelu hudobnej harmónie na základe hu- dobnej teórie. Definujeme nové koncepty: harmonickú zložitost', akordické vzdialenosti a vzdialenosti medzi chroma vektormi. Ukážeme ako tieto kon- cepty využit' pre vyhl'adávanie, analýzu a porovnáme náš model s modernými modelmi hudobnej kognície. V d'alšej časti sa venujeme dôkladnému porov- naniu harmonických...Katedra softwarového inženýrstvíDepartment of Software EngineeringMatematicko-fyzikální fakultaFaculty of Mathematics and Physic