Led by the success of neural style transfer on visual arts, there has been a rising trend very recently in the effort of music style transfer. However, "music style" is not yet a well-defined concept from a scientific point of view. The difficulty lies in the intrinsic multi-level and multi-modal character of music representation (which is very different from image representation). As a result, depending on their interpretation of "music style", current studies under the category of "music style transfer", are actually solving completely different problems that belong to a variety of sub-fields of Computer Music. Also, a vanilla end-to-end approach, which aims at dealing with all levels of music representation at once by directly adopting the method of image style transfer, leads to poor results. Thus, we see a vital necessity to re-define music style transfer more precisely and scientifically based on the uniqueness of music representation, as well as to connect different aspects of music style transfer with existing well-established sub-fields of computer music studies. Otherwise, an accumulated upcoming literature (all named after music style transfer) will lead to a great confusion of the underlying problems as well as negligence of the treasures in computer music before the age of deep learning. In addition, we discuss the current limitations of music style modeling and its future directions by drawing spirit from some deep generative models, especially the ones using unsupervised learning and disentanglement techniques.
As far as we know, there is no work on performance style transfer yet, though performer identification @cite_3 @cite_31 has been studied for over a decade. One close attempt is the recent Duet Interaction system @cite_25 that can generate an expressive accompaniment based on the performance style of a solo, but it requires a pre-defined score and cannot create new performance styles. As a side note, the work named after neural translation of musical style'' @cite_38 is actually an expressive performance rendering system, which focuses on dynamic generation given a score input. Thus, performance style transfer remains a brand-new field worth exploring.
@cite_38 @cite_31 @cite_25 @cite_3
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This paper proposes a system for performance rendering of keyboard instruments. The goal is fully autonomous rendition of a performance with musical smoothness without losing any of the characteristics of the actual performer. The system is based on a method that systematizes combinations of constraints and thereby elucidates the rendering process of the performer’s performance by defining stochastic models that associate artistic deviations observed in a performance with the contextual information notated in its musical score. The proposed system can be used to search for a sequence of optimum cases from the combination of all existing cases of the existing performance observed to render an unseen performance efficiently. Evaluations conducted indicate that musical features expected in existing performances are transcribed appropriately in the performances rendered by the system. The evaluations also demonstrate that the system is able to render performances with natural expressions stably, even for compositions with unconventional styles. Consequently, performances rendered via the proposed system have won first prize in the autonomous section of a performance rendering contest for computer systems. Machine improvisation and related style simulation problems usually consider building repre- sentations of time-based media data, such as music, either by explicit coding of rules or applying machine learning methods. Stylistic learning applies such methods to musical sequences in order to capture salient musical features and organize these features into a model. The Stylistic simulation process browses the model in order to generate variant musical sequences that are stylistically consistent with the learned ma- terial. If both the learning process and the simulation process happen in real-time, in an interactive system where the computer “plays” with musicians, then Machine Improvisation is achieved. Improvisation models have to cope with a trade-off between completeness (all the possible patterns and their continuation laws are discovered) and incrementality (the completeness is ensured only asymptotically for infinite sequences). In a previous work we devised a complete and incremental model based on the Factor Oracle Algorithm. In this paper we propose a concurrent constraints model for the Factor Oracle and show how it can be used in a concurrent learning improvisation situation. Our model is based on a non-deterministic concurrent constraint process calculus (NTCC). Such an approach allows the system to respond in a faster and more flexible manner to real-life performance situations. In addition, the declarative nature of constraints greatly simplifies the expansion of the system with improvisation rules at a higher musical level. We also describe the implementation of our model in a NTCC interpreter written in Common Lisp that is capable of real time performance. The capacity for real-time synchronization and coordination is a common ability among trained musicians performing a music score that presents an interesting challenge for machine intelligence. Compared to speech recognition, which has influenced many music information retrieval systems, music's temporal dynamics and complexity pose challenging problems to common approximations regarding time modeling of data streams. In this paper, we propose a design for a real-time music-to-score alignment system. Given a live recording of a musician playing a music score, the system is capable of following the musician in real time within the score and decoding the tempo (or pace) of its performance. The proposed design features two coupled audio and tempo agents within a unique probabilistic inference framework that adaptively updates its parameters based on the real-time context. Online decoding is achieved through the collaboration of the coupled agents in a Hidden Hybrid Markov semi-Markov framework, where prediction feedback of one agent affects the behavior of the other. We perform evaluations for both real-time alignment and the proposed temporal model. An implementation of the presented system has been widely used in real concert situations worldwide and the readers are encouraged to access the actual system and experiment the results. Abstract We present a system for the automatic transcription of solo human singing into note sequences. Particular attention is devoted to the modelling of the transitions between consecutive notes of the sung passage. Hidden Markov models (HMMs) are used to represent both individual notes and the transitions between them, in order to capture the variability of the estimated pitch within a statistical framework. A database consisting of annotated passages sung by 26 soprano subjects is compiled and used for the development of the system. Explicit transition models are introduced in order to better identify note boundaries that are otherwise poorly modelled. Context-independent transition models are evaluated first, followed by context-dependent transition models which are shown to improve both the note recognition accuracy and the transition region time-alignment. The final system is found to be able to transcribe sung passages with an overall accuracy of 86.7 .