Music Information Retrieval – Soft Computing Versus Statistics

One of the goals of the music information retrieval (MIR) community is to research new methods and create new systems that can efficiently and effectively retrieve and recommend songs from large databases of music content. Despite the volume of research in the area, there is a lack of music databases to support these works, i.e. databases that comply with some quite desirable requirements for the development of researches, such as: a huge amount of music pieces, the audio signal availability and a great diversity of audio attributes. In order to contribute to the MIR community, we present Music4All, a new music database which contains metadata, tags, genre information, 30-seconds audio clips, lyrics, and so on. Additionally, we also exemplify some MIR tasks that may benefit from our database and compare it with other databases proposed in the literature. To request the Music4All database, send an e-mail to contact4music4all@gmail.com.

One of the goals of the music information retrieval (MIR) community is to research new methods and create new systems that can efficiently and effectively retrieve and recommend songs from large databases of music content. Despite the volume of research in the area, there is a lack of music databases to support these works, i.e. databases that comply with some quite desirable requirements for the development of researches, such as: a huge amount of music pieces, the audio signal availability and a great diversity of audio attributes. In order to contribute to the MIR community, we present Music4All, a new music database which contains metadata, tags, genre information, 30-seconds audio clips, lyrics, and so on. Additionally, we also exemplify some MIR tasks that may benefit from our database and compare it with other databases proposed in the literature. To request the Music4All database, send an e-mail to contact4music4all@gmail.com.

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.

Music information retrieval (MIR) systems tend to fall into two camps: that camp developing cataloging and providing advanced access systems for large collections of music and that camp developing specific query or access mechanisms. We have started to merge these camps by integrating Variations2, which provides access to a digitized portion of Indiana University's vast music library, with Michigan's VocalSearch, which provides a query-by-humming (QBH) search engine. The joint system, V2V, demonstrates how QBH can be used in connection with a large number of holdings in a real-world environment.

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 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.

Combining evidences from magnitude and phase information using VTEO for person recognition using humming

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.

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 three packages libACA, pyACA, and ACA-Code provide reference implementations for basic approaches and algorithms for the analysis of musical audio signals in three different languages: C++, Python, and Matlab. All three packages cover the same algorithms, such as extraction of low level audio features, fundamental frequency estimation, as well as simple approaches to chord recognition, musical key detection, and onset detection. In addition, implementations of more generic algorithms useful in audio content analysis such as dynamic time warping and the Viterbi algorithm are provided. The three packages thus provide a practical cross-language and cross-platform reference to students and engineers implementing audio analysis algorithms and enable implementation-focused learning of algorithms for audio content analysis and music information retrieval.

We present the open-source implementation of the first fully automatic and comprehensive DJ system, able to generate seamless music mixes using songs from a given library much like a human DJ does.The proposed system is built on top of several enhanced music information retrieval (MIR) techniques, such as for beat tracking, downbeat tracking, and structural segmentation, to obtain an understanding of the musical structure. Leveraging the understanding of the music tracks offered by these state-of-the-art MIR techniques, the proposed system surpasses existing automatic DJ systems both in accuracy and completeness. To the best of our knowledge, it is the first fully integrated solution that takes all basic DJing best practices into account, from beat and downbeat matching to identification of suitable cue points, determining a suitable cross-fade profile and compiling an interesting playlist that trades off innovation with continuity.To make this possible, we focused on one specific sub-genre of electronic dance music, namely Drum and Bass. This allowed us to exploit genre-specific properties, resulting in a more robust performance and tailored mixing behavior.Evaluation on a corpus of 160 Drum and Bass songs and an additional hold-out set of 220 songs shows that the used MIR algorithms can annotate 91% of the songs with fully correct annotations (tempo, beats, downbeats, and structure for cue points). On these songs, the proposed song selection process and the implemented DJing techniques enable the system to generate mixes of high quality, as confirmed by a subjective user test in which 18 Drum and Bass fans participated.

Musical themes are essential elements in Western classical music. In this paper, we present the Musical Theme Dataset (MTD), a multimodal dataset inspired by “A Dictionary of Musical Themes” by Barlow and Morgenstern from 1948. For a subset of 2067 themes of the printed book, we created several digital representations of the musical themes. Beyond graphical sheet music, we provide symbolic music encodings, audio snippets of music recordings, alignments between the symbolic and audio representations, as well as detailed metadata on the composer, work, recording, and musical characteristics of the themes. In addition to the data, we also make several parsers and web-based interfaces available to access and explore the different modalities and their relations through visualizations and sonifications. These interfaces also include computational tools, bridging the gap between the original dictionary and music information retrieval (MIR) research. The dataset is of relevance for various subfields and tasks in MIR, such as cross-modal music retrieval, music alignment, optical music recognition, music transcription, and computational musicology.

Music emotion recognition is one of the best attractive researches in music information retrieval. To develop an efficient music information retrieval system, a music data set related to music emotion is required. In this paper, we released two music emotion data sets, and proposed a feature selection algorithm that can be useful to investigate the relation between musical property and multiple emotions.

Comunicació presentada a la 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), celebrada els dies 22 a 29 d'octubre de 2017 a Venècia, Itàlia.