{"title":"Score-Informed Source Separation for Music Signals","authors":"Sebastian Ewert, Meinard Müller","doi":"10.4230/DFU.Vol3.11041.73","DOIUrl":"https://doi.org/10.4230/DFU.Vol3.11041.73","url":null,"abstract":"In recent years, the processing of audio recordings by exploiting additional musical knowledge has turned out to be a promising research direction. In particular, additional note information as specified by a musical score or a MIDI file has been employed to support various audio processing tasks such as source separation, audio parameterization, performance analysis, or instrument equalization. In this contribution, we provide an overview of approaches for score-informed source separation and illustrate their potential by discussing innovative applications and interfaces. Additionally, to illustrate some basic principles behind these approaches, we demonstrate how score information can be integrated into the well-known non-negative matrix factorization (NMF) framework. Finally, we compare this approach to advanced methods based on parametric models.","PeriodicalId":400865,"journal":{"name":"Multimodal Music Processing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114194704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Audio Content-Based Music Retrieval","authors":"Peter Grosche, Meinard Müller, J. Serrà","doi":"10.4230/DFU.Vol3.11041.157","DOIUrl":"https://doi.org/10.4230/DFU.Vol3.11041.157","url":null,"abstract":"The rapidly growing corpus of digital audio material requires novel \u0000retrieval strategies for exploring large music collections. Traditional retrieval strategies rely on metadata that describe the actual audio content in words. In the case that such textual descriptions are not available, one requires content-based retrieval strategies which only utilize the raw audio material. In this contribution, we discuss content-based retrieval strategies that \u0000follow the query-by-example paradigm: given an audio query, the task is to retrieve all documents that are somehow similar or related to the query from a music collection. Such strategies can be loosely classified according to their \"specificity\", which refers to the degree of similarity between the query and the database documents. Here, high specificity refers to a strict notion of similarity, whereas low specificity to a rather vague one. Furthermore, we introduce a second classification principle based on \"granularity\", where one distinguishes between fragment-level and document-level retrieval. Using a classification scheme based on specificity and granularity, we identify various classes of retrieval scenarios, which comprise \"audio identification\", \"audio matching\", and \"version \u0000identification\". For these three important classes, we give an overview of representative state-of-the-art approaches, which also illustrate the sometimes subtle but crucial differences between the retrieval scenarios. Finally, we give an outlook on a user-oriented retrieval system, which combines the various retrieval strategies in a unified framework.","PeriodicalId":400865,"journal":{"name":"Multimodal Music Processing","volume":"27 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130973144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Data-Driven Sound Track Generation","authors":"Meinard Müller, Jonathan Driedger","doi":"10.4230/DFU.Vol3.11041.175","DOIUrl":"https://doi.org/10.4230/DFU.Vol3.11041.175","url":null,"abstract":"Background music is often used to generate a specific atmosphere or to draw our attention to specific events. For example in movies or computer games it is often the accompanying music that conveys the emotional state of a scene and plays an important role for immersing the viewer or player into the virtual environment. In view of home-made videos, slide shows, and other consumer-generated visual media streams, there is a need for computer-assisted tools that allow users to generate aesthetically appealing music tracks in an easy and intuitive way. In this contribution, we consider a data-driven scenario where the musical raw material is given in form of a database containing a variety of audio recordings. Then, for a given visual media stream, the task consists in identifying, manipulating, overlaying, concatenating, and blending suitable music clips to generate a music stream that satisfies certain constraints imposed by the visual data stream and by user specifications. It is our main goal to give an overview of various content-based music processing and retrieval techniques that become important in data-driven sound track generation. In particular, we sketch a general pipeline that highlights how the various techniques act together and come into play when generating musically plausible transitions between subsequent music clips.","PeriodicalId":400865,"journal":{"name":"Multimodal Music Processing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129723252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Verena Thomas, C. Fremerey, Meinard Müller, M. Clausen
{"title":"Linking Sheet Music and Audio - Challenges and New Approaches","authors":"Verena Thomas, C. Fremerey, Meinard Müller, M. Clausen","doi":"10.4230/DFU.Vol3.11041.1","DOIUrl":"https://doi.org/10.4230/DFU.Vol3.11041.1","url":null,"abstract":"Score and audio files are the two most important ways to represent, \u0000convey, record, store, and experience music. While score describes a piece of music on an abstract level using symbols such as notes, keys, and measures, audio files allow for reproducing a specific acoustic realization of the piece. Each of these representations reflects different facets of music yielding insights into aspects ranging from structural elements (e.g., motives, themes, musical form) to specific performance aspects (e.g., artistic shaping, \u0000sound). Therefore, the simultaneous access to score and audio \u0000representations is of great importance. \u0000 \u0000In this paper, we address the problem of automatically generating \u0000musically relevant linking structures between the various data sources \u0000that are available for a given piece of music. In particular, we discuss the task of sheet music-audio synchronization with the aim to link regions in images of scanned scores to musically corresponding sections in an audio recording of the same piece. Such linking structures form the basis for novel interfaces that allow users to access and explore multimodal sources of music within a single framework. \u0000 \u0000As our main contributions, we give an overview of the state-of-the-art for this kind of synchronization task, we present some novel approaches, and indicate future research directions. In particular, we address problems that arise in the presence of structural differences and discuss challenges when applying optical music recognition to complex orchestral scores. Finally, potential applications of the synchronization results are presented.","PeriodicalId":400865,"journal":{"name":"Multimodal Music Processing","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117288898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Cross-Version Approach for Harmonic Analysis of Music Recordings","authors":"Verena Konz, Meinard Müller","doi":"10.4230/DFU.Vol3.11041.53","DOIUrl":"https://doi.org/10.4230/DFU.Vol3.11041.53","url":null,"abstract":"The automated extraction of chord labels from audio recordings is a central task in music information retrieval. Here, the chord labeling is typically performed on a specific audio version of a piece of music, produced under certain recording conditions, played on specific instruments and characterized by individual styles of the musicians. As a consequence, the obtained chord labeling results are strongly influenced by version-dependent characteristics. In this chapter, we show that analyzing the harmonic properties of several audio versions synchronously stabilizes the chord labeling result in the sense that inconsistencies indicate version-dependent characteristics, whereas consistencies across several versions indicate harmonically stable passages in the piece of music. In particular, we show that consistently labeled passages often correspond to correctly labeled passages. Our experiments show that the cross-version labeling procedure significantly increases the precision of the result while keeping the recall at a relatively high level. Furthermore, we introduce a powerful visualization which reveals the harmonically stable passages on a musical time axis specified in bars. Finally, we demonstrate how this visualization facilitates a better understanding of classification errors and may be used by music experts as a helpful tool for exploring harmonic structures.","PeriodicalId":400865,"journal":{"name":"Multimodal Music Processing","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126222622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Human Computer Music Performance","authors":"R. Dannenberg","doi":"10.4230/DFU.Vol3.11041.121","DOIUrl":"https://doi.org/10.4230/DFU.Vol3.11041.121","url":null,"abstract":"Human Computer Music Performance (HCMP) is the study of music performance by live human performers and real-time computer-based performers. One goal of HCMP is to create a highly autonomous artificial performer that can fill the role of a human, especially in a popular music setting. This will require advances in automated music listening and understanding, new representations for music, techniques for music synchronization, real-time human-computer communication, music generation, sound synthesis, and sound diffusion. Thus, HCMP is an ideal framework to motivate and integrate advanced music research. In addition, HCMP has the potential to benefit millions of practicing musicians, both amateurs and professionals alike. The vision of HCMP, the problems that must be solved, and some recent progress are presented.","PeriodicalId":400865,"journal":{"name":"Multimodal Music Processing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114867645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fusion of Multimodal Information in Music Content Analysis","authors":"S. Essid, G. Richard","doi":"10.4230/DFU.Vol3.11041.37","DOIUrl":"https://doi.org/10.4230/DFU.Vol3.11041.37","url":null,"abstract":"Music is often processed through its acoustic realization. This is restrictive in the sense that music is clearly a highly multimodal concept where various types of heterogeneous information can be associated to a given piece of music (a musical score, musicians' gestures, lyrics, user-generated metadata, etc.). This has recently led researchers to apprehend music through its various facets, giving rise to \"multimodal music analysis\" studies. This article gives a synthetic overview of methods that have been successfully employed in multimodal signal analysis. In particular, their use in music content processing is discussed in more details through five case studies that highlight different multimodal integration techniques. The case studies include an example of cross-modal correlation for music video analysis, an audiovisual drum transcription system, a description of the concept of informed source separation, a discussion of multimodal dance-scene analysis, and an example of user-interactive music analysis. In the light of these case studies, some perspectives of multimodality in music processing are finally suggested.","PeriodicalId":400865,"journal":{"name":"Multimodal Music Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129046856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Weninger, Björn Schuller, Cynthia C. S. Liem, F. Kurth, A. Hanjalic
{"title":"Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines","authors":"F. Weninger, Björn Schuller, Cynthia C. S. Liem, F. Kurth, A. Hanjalic","doi":"10.4230/DFU.Vol3.11041.195","DOIUrl":"https://doi.org/10.4230/DFU.Vol3.11041.195","url":null,"abstract":"The emerging field of Music Information Retrieval (MIR) has been influenced by neighboring domains in signal processing and machine learning, including automatic speech recognition, image processing and text information retrieval. In this contribution, we start with concrete examples for methodology transfer between speech and music processing, oriented on the building blocks of pattern recognition: preprocessing, feature extraction, and classification/decoding. We then assume a higher level viewpoint when describing sources of mutual inspiration derived from text and image information retrieval. We conclude that dealing with the peculiarities of music in MIR research has contributed to advancing the state-of-the-art in other fields, and that many future challenges in MIR are strikingly similar to those that other research areas have been facing.","PeriodicalId":400865,"journal":{"name":"Multimodal Music Processing","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128194266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Frontmatter, Table of Contents, Preface, List of Authors","authors":"Meinard Müller, Masataka Goto, M. Schedl","doi":"10.4230/DFU.Vol3.11041.i","DOIUrl":"https://doi.org/10.4230/DFU.Vol3.11041.i","url":null,"abstract":"","PeriodicalId":400865,"journal":{"name":"Multimodal Music Processing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123852382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Dittmar, Estefanía Cano, J. Abeßer, S. Grollmisch
{"title":"Music Information Retrieval Meets Music Education","authors":"C. Dittmar, Estefanía Cano, J. Abeßer, S. Grollmisch","doi":"10.4230/DFU.Vol3.11041.95","DOIUrl":"https://doi.org/10.4230/DFU.Vol3.11041.95","url":null,"abstract":"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.","PeriodicalId":400865,"journal":{"name":"Multimodal Music Processing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125462739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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