O VER THE PAST 25 YEARS OUR UNDERSTANDING of how music interacts with the human mind and brain has grown rapidly, and multimedia technologies have augmented the ways in which we engage with music. In Embodied Music Cognition and Mediation Technology, Marc Leman examines how these developments might be unified into something that is simultaneously a theory of music cognition and a blueprint for the music mediation technology of the future. Mediation refers to the mappings between the intentions and desires on the part of active musical participants and the technology that renders the music. The main mediating principle elaborated on in the monograph, which is more intellectual discourse than textbook, is rooted in the belief that musical interactions are socially charged, embodied affairs. Thus, individuals understand music in the same way that they understand others’ intentions during social interaction, and expressive intentions are attributed to music because patterns of sonic energy evoke bodily gestures that are meaningful to an individual due to his or her personal history as an active participant within a cultural environment. The first three chapters contextualize the embodied music cognition approach. In Chapter 1, Leman sets the scene by making clear the challenges that face those who are concerned with how subjective musical experiences are linked to physical sound patterns. Chapter 2 then deals with the diversity of paradigms that are relevant to the business of interdisciplinary music research. Here Leman adeptly identifies relationships between trends in music research, such as the emergence of systematic musicology, and landmark developments in the discipline of psychology, such as the advent of the Gestalt school and cognitivism. He also charts the progress made in the fields of technology, information theory, and computational modeling, with well selected philosophical matter visited along the way. This serves as a historical prelude to the birth of the modern embodied cognition paradigm, which asserts that, “knowledge does not emerge from passive perception, but from the need to act in an environment” (p. 43). In Chapter 3, Leman expands upon this ecological theme with a view toward music mediation technology, developing the premise that mediating technology should exploit the way in which individuals naturally engage themselves with music. The ensuing chapters delve into the details of what embodied music cognition means. Chapters 4 and 5 build a case for why it makes sense to think about engagement with music in terms of corporeal articulations and action-based ontologies, and how these lead to pleasurable experiences with music, whereas Chapters 6 and 7 describe how this type of framework might play itself out in musical instrument and music retrieval technologies. Throughout these chapters, Leman articulates a framework in which performer/music/listener interactions can be structured/mediated. The framework contends with the formidable challenges inherent in mapping between the intentions, actions, and percepts of individuals and very specific musical signals. Ultimately, the problem is one of identifying relationships between semantics and musical structure, and then specifying the technological requirements for accomplishing the translation from one to the other. Leman breaks the problem down into three interacting conceptual levels, which he talks about as first-person, second-person, and third-person descriptions. Thirdperson descriptions are objective representations of the structural features of the music, whereas first-person descriptions are subjectively assigned semantic labels that refer to expressive intentions. According to Leman, previous approaches to understanding music (e.g., traditional musicology) have fixated upon these two levels of description without giving adequate treatment to the “rules” that govern the mapping between objective representations and subjective interpretations. Such rules are needed to achieve his scientific goal of developing a complete theory of music, as well as his practical goal of developing a successful mediation technology. The key to Leman’s solution is the proposal that an understanding of musical intentions requires third-person and first-person descriptions to be linked via second-person descriptions, which are corporeal in nature. At this intermediate level, expressive bodily gestures from an individual’s repertoire of actions are used to describe moving sonic forms in a manner that the individual can interpret based on his or

https://www.jstor.org/stable/info/10.1525/mp.2009.26.3.289

Embodied music cognition and mediation technology

We propose the Probabilistic YIN (PYIN) algorithm, a modification of the well-known YIN algorithm for fundamental frequency (F0) estimation. Conventional YIN is a simple yet effective method for frame-wise monophonic F0 estimation and remains one of the most popular methods in this domain. In order to eliminate short-term errors, outputs of frequency estimators are usually post-processed resulting in a smoother pitch track. One shortcoming of YIN is that such post-processing cannot fall back on alternative interpretations of the signal because the method outputs precisely one estimate per frame. To address this problem we modify YIN to output multiple pitch candidates with associated probabilities (PYIN Stage 1). These probabilities arise naturally from a prior distribution on the YIN threshold parameter. We use these probabilities as observations in a hidden Markov model, which is Viterbi-decoded to produce an improved pitch track (PYIN Stage 2). We demonstrate that the combination of Stages 1 and 2 raises recall and precision substantially. The additional computational complexity of PYIN over YIN is low. We make the method freely available online
 1
 as an open source C++ library for Vamp hosts.

/pdf/pyin-a-fundamental-frequency-estimator-using-probabilistic-10xs066z0v.pdf

PYIN: A fundamental frequency estimator using probabilistic threshold distributions

Graphical abstractDisplay Omitted HighlightsMusic classification using spectrograms and Convolutional Neural Networks.Compare results with state of the art in Latin Music Database, ISMIR 2004 and African music collection.Assessing complementarity between Convolutional Neural Networks and classifiers built with hand-crafted features. Music genre recognition based on visual representation has been successfully explored over the last years. Classifiers trained with textural descriptors (e.g., Local Binary Patterns, Local Phase Quantization, and Gabor filters) extracted from the spectrograms have achieved state-of-the-art results on several music datasets. In this work, though, we argue that we can go further with the time-frequency analysis through the use of representation learning. To show that, we compare the results obtained with a Convolutional Neural Network (CNN) with the results obtained by using handcrafted features and SVM classifiers. In addition, we have performed experiments fusing the results obtained with learned features and handcrafted features to assess the complementarity between these representations for the music classification task. Experiments were conducted on three music databases with distinct characteristics, specifically a western music collection largely used in research benchmarks (ISMIR 2004 Database), a collection of Latin American music (LMD database), and a collection of field recordings of ethnic African music. Our experiments show that the CNN compares favorably to other classifiers in several scenarios, hence, it is a very interesting alternative for music genre recognition. Considering the African database, the CNN surpassed the handcrafted representations and also the state-of-the-art by a margin. In the case of the LMD database, the combination of CNN and Robust Local Binary Pattern achieved a recognition rate of 92%, which to the best of our knowledge, is the best result (using an artist filter) on this dataset so far. On the ISMIR 2004 dataset, although the CNN did not improve the state of the art, it performed better than the classifiers based individually on other kind of features.

An evaluation of Convolutional Neural Networks for music classification using spectrograms

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. 
The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field.

/pdf/current-challenges-and-visions-in-music-recommender-systems-1mu9vgk11n.pdf

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRSs) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user’s fingertip. While today’s MRSs considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user–item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art toward solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field.

/pdf/current-challenges-and-visions-in-music-recommender-systems-5djs7nnth9.pdf

Current challenges and visions in music recommender systems research

This paper presents TarsosDSP, a framework for real-time audio analysis and processing. Most libraries and frameworks offer either audio analysis and feature extraction or audio synthesis and processing. TarsosDSP is one of a only a few frameworks that offers both analysis, processing and feature extraction in real-time, a unique feature in the Java ecosystem. The framework contains practical audio processing algorithms, it can be extended easily, and has no external dependencies. Each algorithm is implemented as simple as possible thanks to a straightforward processing pipeline. TarsosDSP's features include a resampling algorithm, onset detectors, a number of pitch estimation algorithms, a time stretch algorithm, a pitch shifting algorithm, and an algorithm to calculate the Constant-Q. The framework also allows simple audio synthesis, some audio effects, and several filters. The Open Source framework is a valuable contribution to the MIR-Community and ideal fit for interactive MIR-applications on Android.

https://biblio.ugent.be/publication/4260455/file/4260477.pdf

TarsosDSP, a Real-Time Audio Processing Framework in Java

Key-finding is a central topic in Western music analysis and development of MIR tools. However, most approaches rely on the Western 12-tone scale, which is not universally used. African music does not follow a fixed tone scale. In order to classify and study African tone scales, we developed a system in which the pitch is first analyzed on a continuous scale. Peak analysis is then applied on these data to extract the actual scale used. This system has been applied to a selection of African music, it allows us to look for similarities using cross-correlation. Thus it provides an interesting tool for query-by-example and database management in collections of ethnic music which can not be simply classified according to keys. Next to this the data can be used for ethnomusicological research. The study of the intervals used in this collection, e.g., gives us evidence for Western influence, with recent recordings having a tendency to use more regular intervals.

https://ismir2009.ismir.net/proceedings/PS3-17.pdf

Exploring African tone scales

This paper presents a method for retrieving music recordings by means of rhythmic similarity in the context of traditional Greek and African music. To this end, Self Similarity Analysis is applied either on the whole recording or on instances of a music thumbnail that can be extracted from the recording with an optional thumbnailing scheme. This type of analysis permits the extraction of a rhythmic signature per music recording. Similarity between signatures is measured with a standard Dynamic Time Warping technique. The proposed method was evaluated on corpora of Greek and African traditional music where human improvisation plays a key role and music recordings exhibit a variety of music meters, tempi and instrumentation.

/pdf/music-retrieval-by-rhythmic-similarity-applied-on-greek-and-1up0tzb8g3.pdf

Music retrieval by rhythmic similarity applied on Greek and African traditional music

This paper presents Tarsos, a modular software platform used to extract and analyse pitch organization in music. With Tarsos pitch estimations are generated from an audio signal and those estimations are processed in order to form musicologically meaningful representations. Tarsos aims to offer a flexible system for pitch analysis through the combination of an interactive user interface, several pitch estimation algorithms, filtering options, immediate auditory feedback and data output modalities for every step. To study the most frequently used pitches, a fine-grained histogram that allows up to 1200 values per octave is constructed. This allows Tarsos to analyse deviations in Western music, or to analyse specific tone scales that differ from the 12 tone equal temperament, common in many non-Western musics. Tarsos has a graphical user interface or can be launched using an api—as a batch script. Therefore, it is fit for both the analysis of individual songs and the analysis of large music corpora...

Tarsos, a Modular Platform for precise Pitch Analysis of Western and non-Western music

The Belgian Royal Museum for Central-Africa (RMCA) holds a large collection of ethnographic artifacts, including a sound archive with music recordings from the early 20th Century up to recently. The archive is one of the biggest and best-documented archives worldwide for the region of Central Africa. An on-going digitisation project is part of a strategy to conserve this archive and make it accessible to the public by (i) the digitisation of the data, and (ii) the application of music information retrieval techniques for the digitised data. While state-of-the-art research in music information retrieval aims to search and retrieve music on the basis of content description, most of the existing tools are designed for Western music collections, without any guarantee that these techniques can be applied to music from other cultures. African music, in particular, creates new challenges for content-based description and information retrieval. This paper describes some general problems regarding the content-based description of African and other non-Western music. It suggests an approach for describing pitch structures which will allow for the description of both Western music and non-Western music.

The Problems and Opportunities of Content-based Analysis and Description of Ethnic Music

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