A Reference Architecture and Score Representation for Popular Music Human-Computer Music Performance Systems

Popular music (characterized by improvised instrumental parts, beat and measure-level organization, and steady tempo) poses challenges for human-computer music performance (HCMP). Pieces of music are typically rearrangeable on-the-fly and involve a high degree of variation from ensemble to ensemble, and even between rehearsal and performance. Computer systems aiming to participate in such ensembles must therefore cope with a dynamic high-level structure in addition to the more traditional problems of beat-tracking, score-following, and machine improvisation. There are many approaches to integrating the components required to implement dynamic human-computer music performance systems. This paper presents a reference architecture designed to allow the typical sub-components (e.g. beat-tracking, tempo prediction, improvisation) to be integrated in a consistent way, allowing them to be combined and/or compared systematically. In addition, the paper presents a dynamic score representation particularly suited to the demands of popular music performance by computer

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Human-Computer Music Performance: From Synchronized Accompaniment to Musical Partner

Live music performance with computers has motivated many research projects in science, engineering, and the arts. In spite of decades of work, it is surprising that there is not more technology for, and a better understanding of the computer as music performer. We review the development of techniques for live music performance and outline our efforts to establish a new direction, Human-Computer Music Performance (HCMP), as a framework for a variety of coordinated studies. Our work in this area spans performance analysis, synchronization techniques, and interactive performance systems. Our goal is to enable musicians to ncorporate computers into performances easily and effectively through a better understanding of requirements, new techniques, and practical, performance-worthy implementations. We conclude with directions for future work

Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years

Harmony and Technology Enhanced Learning

New technologies offer rich opportunities to support education in harmony. In this chapter we consider theoretical perspectives and underlying principles behind technologies for learning and teaching harmony. Such perspectives help in matching existing and future technologies to educational purposes, and to inspire the creative re-appropriation of technologies