Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

Preliminary report on the design of a constraint-based musical planner

This work described in this paper forms part of a wider research project, described in Holland (1989), to find ways of using artificial intelligence methods to encourage and facilitate music composition by musical novices. This paper focusses on the key component of a knowledge-based tutoring system under development to help novices learn to compose and analyse musically 'sensible' chord sequences. This key component is a constraint-based musical planner dubbed 'PLANC'. The musical planner (together with its set of musical 'plans') can be used to construct and analyse chord sequences in terms of musical strategies that can be understood and made use of by complete musical novices. PLANC can generate a class of musically 'interesting' chord sequences that include the chord sequences of many well known existing pieces of music, as well as generating a large space of new 'interesting' sequences. The design of the planner draws on a characterisation of creativity due to Johnson-Laird (1988). The planner is psychologically plausible , though not intended as a detailed cognitive model. An overview of the structure of PLANC is presented, and its suitability for use in a tutoring system is considered. The design of the planner is criticised. Each of the main components of PLANC is analysed: plan variables, constraints, value generators and methods. Much of the 'knowledge' used in PLANC consists of informal musical knowledge: three appendices analyse the different kinds of informal knowledge used. The applicability and value of similar constraint-based mechanisms in intelligent tutors in a wide range of other open-ended domains is considere

Quo vadimus? The 21st Century and multimedia

The concept is related of computer driven multimedia to the NASA Scientific and Technical Information Program (STIP). Multimedia is defined here as computer integration and output of text, animation, audio, video, and graphics. Multimedia is the stage of computer based information that allows access to experience. The concepts are also drawn in of hypermedia, intermedia, interactive multimedia, hypertext, imaging, cyberspace, and virtual reality. Examples of these technology developments are given for NASA, private industry, and academia. Examples of concurrent technology developments and implementations are given to show how these technologies, along with multimedia, have put us at the threshold of the 21st century. The STI Program sees multimedia as an opportunity for revolutionizing the way STI is managed

Interface Design for Empowerment: a Case Study from Music

It is very seldom that psychological theory is applied to human - computer interface design — because very few theories have yet been formulated which are applicable. For the most part designers have to be content to use guidelines and models, which have less applicability. So, the work described in this chapter is unusual, because it describes an interface to a program which teaches about musical harmony, based on psychological theories. The success of that approach is borne out by the fact that the theories suggest the use of a specific style of interface, based on a two-dimensional spatial representation of harmony relationships. This in turn has been shown to be very successful in teaching novice users about harmony

Lisp, Jazz, Aikido -- Three Expressions of a Single Essence

The relation between Science (what we can explain) and Art (what we can't) has long been acknowledged and while every science contains an artistic part, every art form also needs a bit of science. Among all scientific disciplines, programming holds a special place for two reasons. First, the artistic part is not only undeniable but also essential. Second, and much like in a purely artistic discipline, the act of programming is driven partly by the notion of aesthetics: the pleasure we have in creating beautiful things. Even though the importance of aesthetics in the act of programming is now unquestioned, more could still be written on the subject. The field called "psychology of programming" focuses on the cognitive aspects of the activity, with the goal of improving the productivity of programmers. While many scientists have emphasized their concern for aesthetics and the impact it has on their activity, few computer scientists have actually written about their thought process while programming. What makes us like or dislike such and such language or paradigm? Why do we shape our programs the way we do? By answering these questions from the angle of aesthetics, we may be able to shed some new light on the art of programming. Starting from the assumption that aesthetics is an inherently transversal dimension, it should be possible for every programmer to find the same aesthetic driving force in every creative activity they undertake, not just programming, and in doing so, get deeper insight on why and how they do things the way they do. On the other hand, because our aesthetic sensitivities are so personal, all we can really do is relate our own experiences and share it with others, in the hope that it will inspire them to do the same. My personal life has been revolving around three major creative activities, of equal importance: programming in Lisp, playing Jazz music, and practicing Aikido. But why so many of them, why so different ones, and why these specifically? By introspecting my personal aesthetic sensitivities, I eventually realized that my tastes in the scientific, artistic, and physical domains are all motivated by the same driving forces, hence unifying Lisp, Jazz, and Aikido as three expressions of a single essence, not so different after all. Lisp, Jazz, and Aikido are governed by a limited set of rules which remain simple and unobtrusive. Conforming to them is a pleasure. Because Lisp, Jazz, and Aikido are inherently introspective disciplines, they also invite you to transgress the rules in order to find your own. Breaking the rules is fun. Finally, if Lisp, Jazz, and Aikido unify so many paradigms, styles, or techniques, it is not by mere accumulation but because they live at the meta-level and let you reinvent them. Working at the meta-level is an enlightening experience. Understand your aesthetic sensitivities and you may gain considerable insight on your own psychology of programming. Mine is perhaps common to most lispers. Perhaps also common to other programming communities, but that, is for the reader to decide..

Stylistic analysis and recognition of piano sonatas of four composers -- Mozart, Chopin, Debussy, Anton Webern

This thesis describes a system that incorporates techniques developed by musicologists to do stylistic analysis of music, an important applied field in music theory analysis. To do the analysis requires the knowledge of many musicological analysis methods and pattern recognition algorithms that are central issues to this project. In addition, AI techniques of learning were used to improve the whole system\u27s skills. The conclusions reached as a result of this project were that computers can perform musical tasks usually associated exclusively with naturally intelligent musicologists, and that learning techniques can expand and enrich the behavior of musically intelligent systems

Logic-based Modelling of Musical Harmony for Automatic Characterisation and Classification

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorMusic like other online media is undergoing an information explosion. Massive online music stores such as the iTunes Store1 or Amazon MP32, and their counterparts, the streaming platforms, such as Spotify3, Rdio4 and Deezer5, offer more than 30 million6 pieces of music to their customers, that is to say anybody with a smart phone. Indeed these ubiquitous devices offer vast storage capacities and cloud-based apps that can cater any music request. As Paul Lamere puts it7: “we can now have a virtually endless supply of music in our pocket. The ‘bottomless iPod’ will have as big an effect on how we listen to music as the original iPod had back in 2001. But with millions of songs to chose from, we will need help finding music that we want to hear [...]. We will need new tools that help us manage our listening experience.” Retrieval, organisation, recommendation, annotation and characterisation of musical data is precisely what the Music Information Retrieval (MIR) community has been working on for at least 15 years (Byrd and Crawford, 2002). It is clear from its historical roots in practical fields such as Information Retrieval, Information Systems, Digital Resources and Digital Libraries but also from the publications presented at the first International Symposium on Music Information Retrieval in 2000 that MIR has been aiming to build tools to help people to navigate, explore and make sense of music collections (Downie et al., 2009). That also includes analytical tools to suppor