Music generation has generally been focused on either creating scores or interpreting them. We discuss differences between these two problems and propose that, in fact, it may be valuable to work in the space of direct performance generation: jointly predicting the notes and also their expressive timing and dynamics. We consider the significance and qualities of the dataset needed for this. Having identified both a problem domain and characteristics of an appropriate dataset, we show an LSTM-based recurrent network model that subjectively performs quite well on this task. Critically, we provide generated examples. We also include feedback from professional composers and musicians about some of these examples.
@cite_15 , observe that many previous performance rendering systems often consist of many heuristic rules and tend to be complex. It makes [it] difficult to generate and select the useful rules, or perform the optimization of parameters in the rules.'' They thus present a method that uses Gaussian Processes to achieve this, where some parameters can be learned. In their ostensibly simpler system, for each single note, three outputs and corresponding thirteen input features are defined, and three functions each of which returns one of three outputs and receive the thirteen input features, are independently learned''. However, some of these features, too, depend on certain information, e.g. they compute the differences between successive pitches, and this only works in compositions where the voice leading is absolutely clear; in the majority of classical piano repertoire, this is not the case. In Laminae @cite_34 , systematize a set of context-dependent models, building a decision tree which allows rendering a performance by combining contextual information.
@cite_15 @cite_34
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So far, many of the computational models for rendering music performance have been proposed, but they often consist of many heuristic rules and tend to be complex. It makes difficult to generate and select the useful rules, or perform the optimization of parameters in the rules. In this study, we present a new approach that automatically learns a computational model for rendering music performance with score information as an input and the corresponding real performance data as an output. We use a Gaussian Process (GP) incorporated with a Bayesian Committee Machine to reduce naive GP's heavy computation cost, to learn those input-output relationships. We compared three normalized errors: dynamics, attack time and release time between the real and predicted performance by the trained GP to evaluate our proposed scheme. We evaluated the learning ability and the generalization ability. The results show that the trained GP has an acceptable learning ability for 'known' pieces, but show insufficient generalization ability for 'unknown' pieces, suggesting that the GP can learn the expressive music performance without setting many parameters manually, but the size of the current training dataset is not sufficiently large so as to generalize the training pieces to 'unknown' test pieces. 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. The paper presents a new approach to discovering general rules of expressive music performance from real performance data via inductive machine learning. A new learning algorithm is briefly presented, and then an experiment with a very large data set (performances of 13 Mozart piano sonatas) is described. The new learning algorithm succeeds in discovering some extremely simple and general principles of musical performance (at the level of individual notes), in the form of categorical prediction rules. These rules turn out to be very robust and general: when tested on performances by a different pianist and even on music of a different style (Chopin), they exhibit a surprisingly high degree of predictive accuracy. We present a novel system for the automatic extraction of the main melody from polyphonic music recordings. Our approach is based on the creation and characterization of pitch contours, time continuous sequences of pitch candidates grouped using auditory streaming cues. We define a set of contour characteristics and show that by studying their distributions we can devise rules to distinguish between melodic and non-melodic contours. This leads to the development of new voicing detection, octave error minimization and melody selection techniques. A comparative evaluation of the proposed approach shows that it outperforms current state-of-the-art melody extraction systems in terms of overall accuracy. Further evaluation of the algorithm is provided in the form of a qualitative error analysis and the study of the effect of key parameters and algorithmic components on system performance. Finally, we conduct a glass ceiling analysis to study the current limitations of the method, and possible directions for future work are proposed.