A Fully Convolutional Deep Auditory Model for Musical Chord Recognition

Chord recognition systems depend on robust feature extraction pipelines. While these pipelines are traditionally hand-crafted, recent advances in end-to-end machine learning have begun to inspire researchers to explore data-driven methods for such tasks. In this paper, we present a chord recognition system that uses a fully convolutional deep auditory model for feature extraction. The extracted features are processed by a Conditional Random Field that decodes the final chord sequence. Both processing stages are trained automatically and do not require expert knowledge for optimising parameters. We show that the learned auditory system extracts musically interpretable features, and that the proposed chord recognition system achieves results on par or better than state-of-the-art algorithms.Comment: In Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietro sul Mare, Ital

Commentary on the Portfolio of Compositions submitted for the degree of Doctor of Philosophy by Composition

John Goodenough Doctor of Philosophy by Composition Durham University Department of Music 2014 Portfolio Contents 1. Ubi Caritas 2009 - for Violin & Piano 4.36 2. String Quartet 2010 - for String Quartet 5.15 3. Echoes of Poems & Prose 2010 - for small ensemble 32.45 4. Fountains 2011 - for String Quartet 4.45 5. Stato di Cambiamento 2012 - for large ensemble 5.10 6. Triptych 2012 - for small ensemble 5.20 7. Divergenza 2013 - for large orchestra 33.12 Total time 91.03 Other musical examples (not part of the portfolio) Sette archi spezzati 2013 - for small ensemble 5.28 This portfolio has three principal themes. The first, explored with the discussion of Ubi Caritas and the (2010) String Quartet, concerns the interpretation of harmony; that is harmony, plainly being the vertical component in music but having an inbuilt propensity for horizontal movement, including line and counterpoint. In echoes of Poems & Prose, there is a disregard for any horizontal reasoning, harmony is constrained to the point of isolation and focus fundamentally shifts to the chord as 'object'. I consider this 'objective' sense in detail, in subsequent music in this portfolio. A second theme hinges on a discussion of 'musical material' (the term devised by Theodor Adorno); this considered alongside Samuel Beckett's description of a relationship, between 'mess and confusion' (Beckett's terms for material) and the 'form' that contains it. In Echoes of Poems & Prose, I consider material explicitly, in particular the singular sound. With Fountains and Stato di Cambiamento control of the sounds and their overall architecture become increasingly obscure, with issues around form, substantively re-defining the compositional process. A third theme is the consideration of aspects of structure, which become of particular significance in the final pieces Triptych and Divergenza (the term 'structure' being as defined by John Cage). In Triptych, exploration is made of a confining form into which structural material grows; material that yields intensely colourful musical moments. In the final piece Divergenza, the Fibonacci sequence applies a vice-like grip on the material, but as I remove the conceptual dependence on this sequence, the music's intrinsic characteristics of rhythm and character grow to become of central importance

BitBox!:A case study interface for teaching real-time adaptive music composition for video games

Real-time adaptive music is now well-established as a popular medium, largely through its use in video game soundtracks. Commercial packages, such as fmod, make freely available the underlying technical methods for use in educational contexts, making adaptive music technologies accessible to students. Writing adaptive music, however, presents a significant learning challenge, not least because it requires a different mode of thought, and tutor and learner may have few mutual points of connection in discovering and understanding the musical drivers, relationships and structures in these works. This article discusses the creation of ‘BitBox!’, a gestural music interface designed to deconstruct and explain the component elements of adaptive composition through interactive play. The interface was displayed at the Dare Protoplay games exposition in Dundee in August 2014. The initial proof-of- concept study proved successful, suggesting possible refinements in design and a broader range of applications

On the Complex Network Structure of Musical Pieces: Analysis of Some Use Cases from Different Music Genres

This paper focuses on the modeling of musical melodies as networks. Notes of a melody can be treated as nodes of a network. Connections are created whenever notes are played in sequence. We analyze some main tracks coming from different music genres, with melodies played using different musical instruments. We find out that the considered networks are, in general, scale free networks and exhibit the small world property. We measure the main metrics and assess whether these networks can be considered as formed by sub-communities. Outcomes confirm that peculiar features of the tracks can be extracted from this analysis methodology. This approach can have an impact in several multimedia applications such as music didactics, multimedia entertainment, and digital music generation.Comment: accepted to Multimedia Tools and Applications, Springe

Music Mentor

Extra-curricular learning is on the rise, and many are interested in expanding their current knowledge by utilizing the recent increase in educational technology. While many forms of educational technology exist, there are few interactive and engaging platforms that teach music theory. Apps such as Perfect Ear and MyMusicTheory are great for becoming familiar with reading music and recognizing pitches, however, they often become dry with repetition and repeated tasks. By combining existing technologies that can complete real time conversions from raw audio to MIDI, our goal was to gather information such as harmonies, key and compatible chords from the user’s input. Using this data we aimed to create dynamic lesson plans based on user input, rather than using the same repetitive prompts from overused question pools. We were successfully able to generate these lesson plans, however, the lesson plans that we were able to create are somewhat limited. Given time restraints, we struggled to implement the pruning of audio input to match the desired lesson plans, as the recorded notes must match the correct format to generate a successful plan. Furthermore, we were not able to train a reliable voice model to recognize notes before the project was due. Though not fully complete, we successfully created a prototype dynamic lesson plan that can potentially engage users and assist in the learning of music theory if implemented in future technology

Creative Chord Sequence Generation for Electronic Dance Music

This paper describes the theory and implementation of a digital audio workstation plug-in for chord sequence generation. The plug-in is intended to encourage and inspire a composer of electronic dance music to explore loops through chord sequence pattern definition, position locking and generation into unlocked positions. A basic cyclic first-order statistical model is extended with latent diatonicity variables which permits sequences to depart from a specified key. Degrees of diatonicity of generated sequences can be explored and parameters for voicing the sequences can be manipulated. Feedback on the concepts, interface, and usability was given by a small focus group of musicians and music producers.This research was supported by the project I2C8 (Inspiring to Create) which is funded by the European Union's Horizon 2020 Research and Innovation programme under grant agreement number 754401

Sequential decision making in artificial musical intelligence

Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science