A multi-dimensional entropy model of jazz improvisation for music information retrieval.

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years.

This paper introduces the Raga Based Music Classification as a part of Music Information Retrieval (MIR) systems for retrieving musical information. The music has been more than just a sound that entertains us. MIR users like musicology researchers, musicians, music learners, music therapists, etc., require not just the sound but the actual strength of the music that controls our body, motivates us, cures mental illness, etc. The backbone of any form of music is its raga. A raga is a particular combination of notes with certain laws which, when carefully observed, preserves and safeguards their integrity and create wonders in music. The existing MIR systems and audio search engines concentrate on content based audio mining and not ragas. This paper discusses the classification of ragas and how it differs in Carnatic, Hindustani and Western musical forms. It also describes the various features that can be extracted from an audio data to represent a raga. Finally, we propose a system architecture to perform raga based classification of music.

Music Information Retrieval (MIR) is an interdisciplinary research area that covers automated extraction of information from audio signals, music databases and services enabling the indexed information searching. In the early stages the primary focus of MIR was on music information through Query-by-Humming (QBH) applications, i.e. on identifying a piece of music by singing (singing/whistling), while more advanced implementations supporting Query-by-Example (QBE) searching resulted in names of audio tracks, song identification, etc. Both QBH and QBE required several steps, among others an optimized signal parametrization and the soft computing approach. Nowadays, MIR is associated with research based on the content analysis that is related to the retrieval of a musical style, genre or music referring to mood or emotions. Even though, this type of music retrieval called Query-by-Category still needs feature extraction and parametrization optimizing, but in this case search of global online music systems and services applications with their millions of users is based on statistical measures. The paper presents details concerning MIR background and answers a question concerning usage of soft computing versus statistics, namely: why and when each of them should be employed.

The notion of perceptual features is introduced for describing general music properties based on human perception. This is an attempt at rethinking the concept of features, aiming to approach the underlying human perception mechanisms. Instead of using concepts from music theory such as tones, pitches, and chords, a set of nine features describing overall properties of the music was selected. They were chosen from qualitative measures used in psychology studies and motivated from an ecological approach. The perceptual features were rated in two listening experiments using two different data sets. They were modeled both from symbolic and audio data using different sets of computational features. Ratings of emotional expression were predicted using the perceptual features. The results indicate that (1) at least some of the perceptual features are reliable estimates; (2) emotion ratings could be predicted by a small combination of perceptual features with an explained variance from 75% to 93% for the emotional dimensions activity and valence; (3) the perceptual features could only to a limited extent be modeled using existing audio features. Results clearly indicated that a small number of dedicated features were superior to a "brute force" model using a large number of general audio features.

The field of Music Information Retrieval is still very immature although it has been addressed by a substantial number of research projects over past three decades. Many problems are faced in music information retrieval due to the nature of music itself. In this paper, the basic representation of audio and video are discussed along with the challenges of music retrieval. The need for music information is discussed using the statistical survey. The paper challenges the common assumption that searching on the pitch alone is satisfactory and discusses the other features which can aid in searching. This assumption may indeed be right for monophonic music but it is insufficient for polyphonic music. Even in monophonic music the possibility of faulty results can’t be ignored.

Over the years, Music Information Retrieval (MIR) has proposed various models pretrained on large amounts of music data. Transfer learning showcases the proven effectiveness of pretrained backend models with a broad spectrum of downstream tasks, including auto-tagging and genre classification. However, MIR papers generally do not explore the efficiency of pretrained models for Music Recommender Systems (MRS). In addition, the Recommender Systems community tends to favour traditional end-to-end neural network learning over these models. Our research addresses this gap and evaluates the applicability of six pretrained backend models (MusicFM, Music2Vec, MERT, EncodecMAE, Jukebox, and MusiCNN) in the context of MRS. We assess their performance using three recommendation models: K-nearest neighbours (KNN), shallow neural network, and BERT4Rec. Our findings suggest that pretrained audio representations exhibit significant performance variability between traditional MIR tasks and MRS, indicating that valuable aspects of musical information captured by backend models may differ depending on the task. This study establishes a foundation for further exploration of pretrained audio representations to enhance music recommendation systems.

The article describes the project on music information retrieval that has been carried out at the University of Padova, Italy. The research work has been characterized by the synergy of the modular integration of sound techniques of melody processing and of statistical information retrieval. After illustrating the background from which the project has originated, we describe the complete process, from methodology design through evaluation and system implementation. Conclusions, impacts on research in music information retrieval, and future directions are also described.

Music information research (MIR) has been a fast growing field of research during the past decade. In traditional MIR, music-related information were extracted from the audio signal using signal processing techniques. These methods, however, cannot capture semantic information that is not encoded in the audio signal, but nonetheless essential to many consumers, e.g., the meaning of the lyrics of a song or the political motivation or background of a singer. The recent launches of Google Music, Amazon.com's Cloud Player and Apple's iCloud with iTunes' Scan and Match show the huge commercial interest behind music distribution and consumption. Given the fact that music is an omnipresent topic on the Web, techniques to mine the Web of Music are vital for music information research.In recent years, the emergence of various Web 2.0 platforms and services dedicated or related to the music and audio domain, like last.fm, YouTube, MusicBrainz, Pandora, or Echo Nest, has been providing novel and powerful, albeit noisy, sources for high level, semantic information on music artists, albums, songs, and others. The abundance of such information provided by the power of the crowd can therefore contribute to music information research and development considerably. On the other hand, the wealth of newly available, semantically meaningful information offered on Web 2.0 platforms also poses new challenges, e.g., dealing with the huge amount and the noisiness of this kind of data, various user biases, hacking, or the cold start problem.Another recent trend are innovative user interfaces to access the large amounts of music available on smart mobile devices that are always connected to the Web. Dealing with the vast amounts of music requires new interaction paradigms and intelligent services that provide, for example, personalized and context-aware music recommendations. The current emergence and confluence of these challenges make this an interesting field for researchers and industry practitioners alike.Addressing these challenges, the International Workshop on Advances in Music Information Research (AdMIRe) workshop served as a forum for theoretical and practical discussions of cutting edge research in the fields of music information extraction, retrieval, and recommendation in the Web of Music. The workshop brought together researchers and developers from the music and audio community, the multimedia community, and the Web mining community and further initiated interesting and fruitful discussions on the future of MIR.This year was AdMIRe's 4th edition, and we received a considerable number of high-quality submissions. A total of 14 submissions from 7 different countries have been reviewed by at least 4 reviewers each, and 9 papers have finally been accepted for publication and presentation at the workshop. They span a wide field, including topics such as Web-based data and microblogging mining, content-based music processing, music similarity and recommendation from hybrid sources, and new evaluation methods.

SoundSignature is a music application that integrates a custom OpenAI Assistant to analyze users' favorite songs. The system incorporates state-of-the-art Music Information Retrieval (MIR) Python packages to combine extracted acoustic/musical features with the assistant's extensive knowledge of the artists and bands. Capitalizing on this combined knowledge, SoundSignature leverages semantic audio and principles from the emerging Internet of Sounds (IoS) ecosystem, integrating MIR with AI to provide users with personalized insights into the acoustic properties of their music, akin to a musical preference personality report. Users can then interact with the chatbot to explore deeper inquiries about the acoustic analyses performed and how they relate to their musical taste. This interactivity transforms the application, acting not only as an informative resource about familiar and/or favorite songs, but also as an educational platform that enables users to deepen their understanding of musical features, music theory, acoustic properties commonly used in signal processing, and the artists behind the music. Beyond general usability, the application also incorporates several well-established open-source musician-specific tools, such as a chord recognition algorithm (CREMA), a source separation algorithm (DEMUCS), and an audio-to-MIDI converter (basic-pitch). These features allow users without coding skills to access advanced, open-source music processing algorithms simply by interacting with the chatbot (e.g., can you give me the stems of this song?). In this paper, we highlight the application's innovative features and educational potential, and present findings from a pilot user study that evaluates its efficacy and usability.

March 01 2005 A Multi-Layered, Time-Based Music Description Approach Based on XML Goffredo Haus, Goffredo Haus LIM-DICO, Università degli Studi di Milano, Via Comelico 39 20135 Milan, Italy, haus@dico.unimi.it Search for other works by this author on: This Site Google Scholar Maurizio Longari Maurizio Longari LIM-DICO, Università degli Studi di Milano, Via Comelico 39 20135 Milan, Italy, longari@dico.unimi.it Search for other works by this author on: This Site Google Scholar Author and Article Information Goffredo Haus LIM-DICO, Università degli Studi di Milano, Via Comelico 39 20135 Milan, Italy, haus@dico.unimi.it Maurizio Longari LIM-DICO, Università degli Studi di Milano, Via Comelico 39 20135 Milan, Italy, longari@dico.unimi.it Online ISSN: 1531-5169 Print ISSN: 0148-9267 © 2005 Massachusetts Institute of Technology2005 Computer Music Journal (2005) 29 (1): 70–85. https://doi.org/10.1162/comj.2005.29.1.70 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Goffredo Haus, Maurizio Longari; A Multi-Layered, Time-Based Music Description Approach Based on XML. Computer Music Journal 2005; 29 (1): 70–85. doi: https://doi.org/10.1162/comj.2005.29.1.70 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsComputer Music Journal Search Advanced Search This content is only available as a PDF. © 2005 Massachusetts Institute of Technology2005 Article PDF first page preview Close Modal You do not currently have access to this content.

Music Information Retrieval (MIR) is a crucial topic in the domain of information retrieval. According to major characteristics of music, Query-by-Humming system retrieves interesting music by finding melody that contains similar or equal melody to the humming query. Basing on the designed fuzzy inference model a novel Query-by-Humming/Singing system is proposed to extract pitch contour information from WAV and MIDI files in this paper. To verify the effectiveness of the presented work, the MIREX QBSH Database is employed as our experimental database and a large amount of human vocal data is used as query to test the robustness of MIR. Then, the Longest Common Subsequence (LCS) is served as an approximate matching algorithm to identify the most related top 5 music as an evaluation standard for the system. Experimental results show that the proposed system achieves 85% accuracy in the top 5 retrievals.

This paper proposes methods for information processing of audio streams using methods of information geometry. We lay the theoretical groundwork for a framework allowing the treatment of signal information as information entities, suitable for similarity and symbolic computing on audio signals. The theoretical basis of this paper is based on the information geometry of statistical structures representing audio spectrum features, and specifically through the bijection between the generic families of Bregman divergences and that of exponential distributions. The proposed framework, called Music Information Geometry, allows online segmentation of audio streams to metric balls where each ball represents a quasi-stationary continuous chunk of audio, and discusses methods to qualify and quantify information between entities for similarity computing. We define an information geometry that approximates a similarity metric space, redefine general notions in music information retrieval such as similarity between entities, and address methods for dealing with nonstationarity of audio signals. We demonstrate the framework on two sample applications for online audio structure discovery and audio matching.

Large repository of music that can be accessed or downloaded over the Internet, provides a new way of trading or sharing. However, the technologies for features based Music Information Retrieval (MIR), which is a multidisciplinary field of research, are not well established. Existing MIR techniques and products suffer from either limited capabilities or poor performance. In this paper, we proposed a data model that describes the music information using both Music Definition Language (MDL) and Music Manipulation Language (MML), and supports extensible hybrid methods for music classification and similarity measurement. With proposed musical data model, we further developed a hybrid method that combines both contour and rhythm features, and employed an Artificial Neural Network (Unsupervised Kohonen Self-Organized Map) based classification mechanism that maps variations of music pieces to their corresponding originals using a new vector/matrix format defined as MDL. The proposed hybrid method based on a deep-learning mechanism and a new similarity measurement method has been introduced to fulfil analysis on the music classification and their similarity scores. The test results demonstrate that an accuracy of around 70% in the experiments has been achieved.

The continuous integration of computer technology and art has promoted the development of digital music. In recent years, the massive music data has promoted the problems of music inquiry, music classification, music content understanding and so on, making a new subject develop continuously, that is music information retrieval. The current mainstream feature extraction of music is based on acoustics, such as pitch, timbre, loudness, zero-crossing rate, etc. Direct symbolic music feature extraction and music analysis are relatively rare. This paper aims to present an empirical study on text clustering ideas in natural language processing into the field of symbolic music style analysis. Firstly, Symbolic music is textualized and proposed by us as an inspiration. To be precise, textualization of symbolic music is converted into weighted structured data through tf-idf algorithm. In the following step, three different types of mainstream clustering algorithms, K-Means, OPTICS, and Birch are used to perform cluster analysis and comparison on the traditional Chinese folk music dataset we crawled, T-SNE algorithm is used to visualize the dimensionality reduction of high-dimensional data. Finally, a series of objective evaluation indicators of clustering are used to evaluate the three clustering algorithms. Through comprehensive evaluation of indicators, it is proved that the clustering algorithm has achieved an excellent clustering effect on the midi note dataset we extracted. As a result of the clustering, the professional music theory knowledge and the historical development characteristics of traditional Chinese folk music are comprehensively integrated, which reversely verifies that the 1300 midi music data sets have distinct modal characteristics of traditional Chinese folk music.

Representing a significant shift from methods that employ bibliographic metadata for music retrieval, modern Music Information Retrieval (MIR) research seeks to utilize characteristics of music to search databases of musical content via musically expressed queries. These investigations hold significant promise for the future development of commercial and research applications such as searchable databases of popular tunes on the web and databases of historical music. However the challenges are diverse, encompassing music cognition, storage and processing, and traditional information retrieval (IR) issues. This paper focuses on IR issues in MIR, discussing various approaches to music representation and matching techniques. Prototype MIR systems Meldex, Semex, Themefinder and Arthur are briefly described.