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.
Two centuries later, as the foundations of AI were being set, the notion of automatically understanding (and therefore generating) music was among the earliest applications to capture the imagination of researchers, with papers on computational approaches to perception, interpretation and generation of music by Simon, Longuet-Higgins and others @cite_4 @cite_12 @cite_30 @cite_28 @cite_6 . Since then, many interesting efforts were made @cite_42 @cite_18 @cite_22 @cite_2 @cite_11 @cite_17 , and it is clear that in recent years both interest and progress in score generation has continued to advance, e.g. @cite_38 , Boulanger- @cite_32 , @cite_0 , @cite_41 , @cite_19 , Sturm @cite_5 , to name only a few. @cite_20 provide a survey of generative music models that involve machine learning. @cite_43 provide a comprehensive survey and satisfying taxonomy of music generation systems. McDonald @cite_3 gives an overview highlighting some key examples of such work.
@cite_30 @cite_22 @cite_41 @cite_42 @cite_3 @cite_43 @cite_2 @cite_5 @cite_20 @cite_38 @cite_18 @cite_4 @cite_17 @cite_28 @cite_32 @cite_6 @cite_19 @cite_12 @cite_0 @cite_11
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Machine learning models of music typically break down the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. We explore the use of blocked Gibbs sampling as an analogue to the human approach, and introduce Coconet, a convolutional neural network in the NADE family of generative models. Despite ostensibly sampling from the same distribution as the NADE ancestral sampling procedure, we find that a blocked Gibbs approach significantly improves sample quality. We provide evidence that this is due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from (2014) yields better samples than ancestral sampling. We demonstrate the versatility of our method on unconditioned polyphonic music generation. From the Publisher: Can humans compute? This is the question to which H. Christopher Longuet-Higgins, one of the founding figures of cognitive science, has devoted his research over the past twenty years. His and his field's intellectual odyssey from the fringe to the center of the scientific world's attention is recounted with wit and grace in this wide-ranging collection of previously published and original essays. The volume begins in the late 1960s, when the author had moved from theoretical chemistry to what was then known as theoretical biology. It traces his search for new concepts with which to establish a science of the mind, and it includes Longuet-Higgins's famous comment on the 1971 Lighthill Report in which he introduced the term "cognitive science" and sketched the possible components of the field. The essays are divided into five parts. The first, Generalities, explores the basic philosophical questions at the root of the new science. The essays on Music show the importance of the musical sense as a testing ground for understanding cognitive processes in general. The author's forays into Language describe some of the major early achievements in the now very active field of computational linguistics. The studies of Vision are all directed to the problem - crucial for the development of machine-vision systems - of inferring the structure of a scene from two views. The author suggests that the chapters on Memory "be treated indulgently as the first attempt of a physical scientist to climb out of the mindless world of atoms and molecules into the real world of subjective experience." H. Christopher Longuet-Higgins is Royal Society ResearchProfessor at the University of Sussex. Mental Processes inaugurates the series Explorations in Cognitive Science, edited by Margaret Boden and co-sponsored by The MIT Press and The British Psychological Society. A Bradford Book. Research on automatic music generation has seen great progress due to the development of deep neural networks. However, the generation of multi-instrument music of arbitrary genres still remains a challenge. Existing research either works on lead sheets or multi-track piano-rolls found in MIDIs, but both musical notations have their limits. In this work, we propose a new task called lead sheet arrangement to avoid such limits. A new recurrent convolutional generative model for the task is proposed, along with three new symbolic-domain harmonic features to facilitate learning from unpaired lead sheets and MIDIs. Our model can generate lead sheets and their arrangements of eight-bar long. Source code and audio samples of the generated result can be found at the project webpage: https: liuhaumin. github.io LeadsheetArrangement This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument note annotations resulting in over 1 million temporal labels on 34 hours of chamber music performances under various studio and microphone conditions. @PARASPLIT The paper defines a multi-label classification task to predict notes in musical recordings, along with an evaluation protocol, and benchmarks several machine learning architectures for this task: i) learning from spectrogram features; ii) end-to-end learning with a neural net; iii) end-to-end learning with a convolutional neural net. These experiments show that end-to-end models trained for note prediction learn frequency selective filters as a low-level representation of audio.