The Sound/Music Dilemma: Why Is It That All Music Is Sound but Only Some Sounds Are Music?

Even if sound and music are deeply intertwined phenomena, it is still fiercely debated whether all music is made up of sound, and vice versa, whether all sound can be deemed as music. Researchers from many different backgrounds have proposed numerous solutions to the conundrum; however, most of them analyse music and sound out of their natural context, ignoring not negligible variables such as listeners and cognitive constraints. This paper fills this gap, by introducing a theoretical model, called the Circle of Sound, which aims to solve the sound/music dilemma. The Circle of Sound is based on cognition, and has been developed according to the concepts of musical complexity, musical understanding and musical enjoyment. By combining these concepts, a new operational definition of music is proposed. The theoretical framework provided can also be used as a basis for future experimental investigations, willing to shed some light on how musical is understood by people

A Planning-based Approach for Music Composition

. Automatic music composition is a fascinating field within computational creativity. While different Artificial Intelligence techniques have been used for tackling this task, Planning – an approach for solving complex combinatorial problems which can count on a large number of high-performance systems and an expressive language for describing problems – has never been exploited. In this paper, we propose two different techniques that rely on automated planning for generating musical structures. The structures are then filled from the bottom with “raw” musical materials, and turned into melodies. Music experts evaluated the creative output of the system, acknowledging an overall human-enjoyable trait of the melodies produced, which showed a solid hierarchical structure and a strong musical directionality. The techniques proposed not only have high relevance for the musical domain, but also suggest unexplored ways of using planning for dealing with non-deterministic creative domains

The Effect of Repetition and Expertise on Liking and Complexity in Contemporary Music

Aesthetic perception of music has been extensively researched in the last decades. Numerous studies suggest that listeners find a piece of music more or less pleasant according to its complexity. Experimental results show that complexity and liking have different relationship according to the musical genre examined, and that these two variables are also affected by other factors such as familiarity to the music and expertise of the listener. Although previous experiments have examined several genres such as jazz, pop, rock and bluegrass, surprisingly, no study has focused on contemporary music. In this paper, we fill this gap by studying the relationships between complexity, liking, musical training and familiarity in the case of contemporary music. By analysing this genre – which is usually underrepresented in music cognition – it is possible to shed some light on the correlation between liking and complexity in the case of highly complex music. To obtain data, a multifactor experiment was designed in which both music experts and novices had to provide scores of subjective complexity and liking for four 30-second long excerpts of contemporary music with different degrees of complexity. Empirical results suggest that liking and complexity are negatively correlated in the case of contemporary music and that listeners’ expertise does not influence the perceived complexity of musical pieces, but it can significantly affect liking. This possibly indicates that experts have the musical knowledge needed to appreciate extremely complex music, while novices do not

GenoMeMeMusic: A Memetic-based Framework for Discovering the Musical Genome

(Abstract to follow

Symbolic Melodic Similarity: State of the Art and Future Challenges

Fostered by the introduction of the Music Information Retrieval Evaluation eXchange (MIREX) competition, the number of systems which calculate Symbolic Melodic Similarity has recently increased considerably. In order to understand the state of the art, we provide a comparative analysis of existing algorithms. The analysis is based on eight criteria that help characterising the systems, and highlighting strengths and weaknesses. We also propose a taxonomy which classifies algorithms based on their approach. Both taxonomy and criteria are fruitfully exploited for providing input for new forthcoming research in the area

Automatic Melody Composition and Evolution: A Cognitive-Based Approach

Music composition is a highly interdisciplinary process. To understand it deeply, a number of approaches have been used from different fields, such as musicology, music theory, music cognition and philosophy. During recent decades, numerous techniques based on Artificial Intelligence (AI) have been proposed. In particular, many AI systems focus on automatic melodic composition. Most of these systems try to generate melodies enjoyable by a human, but they completely ignore the way in which humans actually compose. Humans create music by exploiting a mixed top-down bottom-up approach, characterised by high-level cognition processes and rules. In this paper, we propose a memetic model for music composition, which considers both psychological and social levels. The former level analyses the actual cognitive mechanisms and procedures involved while composing music: namely, museme network, compositional grammar and evaluation module. The social level puts the figure of the composer into perspective within her musical environment. The introduced memetic model is encoded in a two-step algorithm. Firstly, a top-down approach is used for defining the overall structure of a melody. Secondly, the given structure is filled with musical content, following a bottom-up strategy, that fosters emergent behaviour. The proposed algorithm is the first system we are aware of which can evolve its own compositional style. Stylistic change is achieved by modifying grammar rules and the museme network. Finally, the paper provides an analysis of generated melodies