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.
Moulieras and Pachet @cite_27 use a maximum entropy model to generate expressive music, but their focus is again monophonic plus simple harmonic information. They also explicitly assume that musical expression consists in local texture, rather than long-range correlations''. While this is fairly reasonable at this point, and indeed it is hard to say how much long-range correlation is captured by our model, we wished to choose a model which, at least in principle, allowed the possibility of modeling long-range correlation: ultimately, we believe that these correlations are of fundamental importance. Malik and Ek @cite_7 use a neural network to learn to predict the dynamic levels of individual notes while assuming quantized and steady timing.
@cite_27 @cite_7
2530415313 2553011065 2746068898 2962942158
In the context of contemporary monophonic music, expression can be seen as the difference between a musical performance and its symbolic representation, i.e. a musical score. In this paper, we show how Maximum Entropy (MaxEnt) models can be used to generate musical expression in order to mimic a human performance. As a training corpus, we had a professional pianist play about 150 melodies of jazz, pop, and latin jazz. The results show a good predictive power, validating the choice of our model. Additionally, we set up a listening test whose results reveal that on average, people significantly prefer the melodies generated by the MaxEnt model than the ones without any expression, or with fully random expression. Furthermore, in some cases, MaxEnt melodies are almost as popular as the human performed ones. We introduce a model for music generation where melodies are seen as a network of interacting notes. Starting from the principle of maximum entropy we assign to this network a probability distribution, which is learned from an existing musical corpus. We use this model to generate novel musical sequences that mimic the style of the corpus. Our main result is that this model can reproduce high-order patterns despite having a polynomial sample complexity. This is in contrast with the more traditionally used Markov models that have an exponential sample complexity. Most existing neural network models for music generation use recurrent neural networks. However, the recent WaveNet model proposed by DeepMind shows that convolutional neural networks (CNNs) can also generate realistic musical waveforms in the audio domain. Following this light, we investigate using CNNs for generating melody (a series of MIDI notes) one bar after another in the symbolic domain. In addition to the generator, we use a discriminator to learn the distributions of melodies, making it a generative adversarial network (GAN). Moreover, we propose a novel conditional mechanism to exploit available prior knowledge, so that the model can generate melodies either from scratch, by following a chord sequence, or by conditioning on the melody of previous bars (e.g. a priming melody), among other possibilities. The resulting model, named MidiNet, can be expanded to generate music with multiple MIDI channels (i.e. tracks). We conduct a user study to compare the melody of eight-bar long generated by MidiNet and by Google's MelodyRNN models, each time using the same priming melody. Result shows that MidiNet performs comparably with MelodyRNN models in being realistic and pleasant to listen to, yet MidiNet's melodies are reported to be much more interesting. Realistic music generation is a challenging task. When building generative models of music that are learnt from data, typically high-level representations such as scores or MIDI are used that abstract away the idiosyncrasies of a particular performance. But these nuances are very important for our perception of musicality and realism, so in this work we embark on modelling music in the raw audio domain. It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations. This is problematic because music exhibits structure at many different timescales. In this work, we explore autoregressive discrete autoencoders (ADAs) as a means to enable autoregressive models to capture long-range correlations in waveforms. We find that they allow us to unconditionally generate piano music directly in the raw audio domain, which shows stylistic consistency across tens of seconds.