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The purpose of this paper is to better understand communication between musicians in a free jazz improvisation in comparison to traditional jazz.

A cybernetic informative feedback model was used to study communication between musicians for free jazz. The conceptual model consists of the ears as sensors, an auditory analysis stage to convert the acoustic signals into symbolic information (e.g. notated music), a cognitive processing stage (to make decisions and adapt the performance to what is being heard), and an effector (e.g. muscle movement to control an instrument). It was determined which musical features of the co‐players have to be extracted to be able to respond adequately in a music improvisation, and how this knowledge can be used to build an automated music improvisation system for free jazz.

The three major findings of this analysis were: in traditional jazz a soloist only needs to analyze a very limited set of music ensemble features, but in free jazz the performer has to observe each musician individually; unlike traditional jazz, free jazz is not a strict rule‐based system. Consequently, the musicians need to develop their personal symbolic representation; which could be a machine‐adequate music representation for an automated music improvisation system. The latter could be based on acoustic features that can be extracted robustly by a computer algorithm.

Gained knowledge can be applied to build automated music improvisation systems for free jazz.

The paper expands our knowledge to create intelligent music improvisation algorithms to algorithms that can improvise with a free jazz ensemble.

This article seeks to provide a theoretical framework for facilitating talent management decisions in the music industry.

Strategic decision‐making theory and the resource‐based view of strategy are used to identify the talent‐selection process as a core capability in the entertainment industry. Their original combination leads to the introduction of a framework aimed at facilitating the selection and development of core competencies and capabilities in music companies, and thus at increasing their likelihood of creating and sustaining a competitive advantage based on their artist selection processes.

The integration of both theories in the music sector leads to the need for the organisation's ability to “improvise” and develop “skilled decision makers”. The industry is identified as an atypical high velocity environment, in which incremental approaches may not be sufficient to adopt by managers, because artist investments usually represent long‐term commitments for the firm. Three different existing types of talent valuation techniques are identified, which can be facilitated by complementing resource‐based and decision‐making perspectives.

The paper does not discuss differences in the types of music organisations such as publishers, record companies, labels etc. Moreover, it focuses on popular music in general only. Empirical testing of the proposed findings is needed to further validate the capability framework.

The framework provides a managerial guideline for implementing decision models in the music industry and increasing the success rate of artist selection.

The paper uses the specific context of the music industry to introduce a methodology of how organisational decision processes may eventually lead to a sustainable competitive advantage. It provides a starting point for linking resource‐based and strategic decision‐making theory, since it indicates how decision models should be developed from a core capability perspective.

How can leaders adopt a mindset that maximizes learning, remains responsive to short-term emergent opportunities, and simultaneously strengthens longer-term dynamic capabilities of the organization? This chapter explores the organizational decisions and practices leaders can initiate to extend, strengthen, or transform “ordinary capabilities” (Winter, 2003) into enhanced improvisational competence and dynamic capabilities. We call this leadership logic the “jazz mindset.” We draw upon seven characteristics of jazz bands as outlined by Barrett (1998) to show that strategic leaders of business organizations can enhance dynamic capabilities by strengthening practices observed in improvising jazz bands.

The term “Brazilian popular music” refers to a varied repertoire of musical styles with a strong connection to local culture. The initiatives of representation of this domain of interest occur through adaptations of generic models and strategies coming from contexts and musical styles that differ from the essential characteristics of the national music. The purpose of this paper is to present a characterization of Brazilian popular music as a conceptual model which supports the communication and analysis of this domain and serves as a reference ontology for various applications in the field of Information Science and others.

To achieve the purpose, a mapping about Brazilian popular music was done from a literature review and a data collection with expert users, based on domain analysis theory. From this characterization, the conceptual model was built using an Ontology Engineering approach. To facilitate understanding, the results were described using a more user-friendly notation.

The paper presents a conceptual model as a first semantic reference on Brazilian popular music that serves (1) to better understand, communicate and analyze the domain of Brazilian popular music and, (2) to supply some semantic aspects not covered by the adaptations that have been proposed on the literature for musical representation.

The paper adds a new perspective to the understanding of Brazilian popular music and open opportunity to explore other repertoires about popular music.

If an information retrieval system is going to be of value to the user then it must give meaning to the information which matches the meaning given to it by the user. The meaning given to music varies according to who is interpreting it – the author/composer, the performer, cataloguer or the listener – and this affects how music is organized and retrieved. This paper aims to examine the meaning of music, how meaning is communicated and suggests this may affect music retrieval.

Musicology is used to define music and examine its functions leading to a discussion of how music has been organised and described. Various ways of establishing the meaning of music are reviewed, focussing on established musical analysis techniques. It is suggested that traditional methods are of limited use with digitised popular music. A discussion of semiotics and a review of semiotic analysis in western art music leads to a discussion of semiotics of popular music and examines ideas of Middleton, Stefani and Tagg.

Agreeing that music exists when communication takes place, a discussion of selected communication models leads to the proposal of a revised version of Tagg's model, adjusting it to include listener feedback.

The outcome of the analysis is a revised version of Tagg's communication model, adapted to reflect user feedback. It is suggested that this revised communication model reflects the way in which meaning is given to music.

Not many weeks back, according to newspaper reports, three members of the library staff of the School of Slavonic and East European Studies in London were dismissed. All had refused to carry out issue desk duty. All, according to the newspaper account, were members of ASTMS. None, according to the Library Association yearbook, was a member of the appropriate professional organisation for librarians in Great Britain.

Covid-19 pandemic, war, climate emergency and other recent challenges are inflicting tremendous stress to youth. However, death and tragedy are nowadays considered taboo, as there is generally no standardized nor naturalized discussion on the subject, especially with young people. The current multi-crisis scenario is intensifying the need to incorporate an education on tragedy and resilience in our learning systems. In this context, it is necessary to find suitable teaching resources for this educational challenge that are attractive, entertaining and suitable for children and youth. A resource that meets all these requirements are children’s folk songs (CFSs). Apart from the intrinsic educational potential of music, folk songs have a simplicity and musicality that make them an ideal teaching resource. Considering their oral historical transmission, their survival confirms the attraction that this type of composition causes on children. However, to consider CFSs as an adequate resource to carry out an education for death and tragedy, it is necessary to study whether they present a non-negligible proportion of tragic passages and with enough variety of themes. This paper aims to address the study of the presence of explicit tragic content in Spanish CFSs and thus could be considered a cultural resource with transformative educational potential to develop resilience capabilities on the face of tragedy.

An analysis of lyrics of 2,558 Spanish CFSs is presented, using a manual content analysis as well as a computerized content analysis with the aim of identifying the tragic component of these songs and, thereby, assessing their pedagogical potential as a transformative educational resource.

The results obtained show a considerable presence of death and tragedy (19.78%) and a variety of tragedy dimensions. CFSs have been transmitted orally not only as a ludic resource, but also to prepare children for life (and death). The results show the complementarity of both analyses to avoid subjectivity while considering the underlying meanings of the songs.

This task had previously not been approached in an automated manner in the literature, nor there had been a similar study with a sample of this magnitude. The outcomes obtained show the considerable presence of tragedy in Spanish CFSs and emphasize the interest of this currently undervalued didactic resource.

Genre classification of songs based on lyrics is a challenging task even for humans, however, state-of-the-art natural language processing has recently offered advanced solutions to this task. The purpose of this study is to advance the understanding and application of natural language processing and deep learning in the domain of music genre classification, while also contributing to the broader themes of global knowledge and communication, and sustainable preservation of cultural heritage.

The main contribution of this study is the development and evaluation of various machine and deep learning models for song genre classification. Additionally, we investigated the effect of different word embeddings, including Global Vectors for Word Representation (GloVe) and Word2Vec, on the classification performance. The tested models range from benchmarks such as logistic regression, support vector machine and random forest, to more complex neural network architectures and transformer-based models, such as recurrent neural network, long short-term memory, bidirectional long short-term memory and bidirectional encoder representations from transformers (BERT).

The authors conducted experiments on both English and multilingual data sets for genre classification. The results show that the BERT model achieved the best accuracy on the English data set, whereas cross-lingual language model pretraining based on RoBERTa (XLM-RoBERTa) performed the best on the multilingual data set. This study found that songs in the metal genre were the most accurately labeled, as their text style and topics were the most distinct from other genres. On the contrary, songs from the pop and rock genres were more challenging to differentiate. This study also compared the impact of different word embeddings on the classification task and found that models with GloVe word embeddings outperformed Word2Vec and the learning embedding layer.

This study presents the implementation, testing and comparison of various machine and deep learning models for genre classification. The results demonstrate that transformer models, including BERT, robustly optimized BERT pretraining approach, distilled bidirectional encoder representations from transformers, bidirectional and auto-regressive transformers and XLM-RoBERTa, outperformed other models.