Discovering and exploring the underlying structure of multi-instrumental music using learning-based approaches remains an open problem. We extend the recent MusicVAE model to represent multitrack polyphonic measures as vectors in a latent space. Our approach enables several useful operations such as generating plausible measures from scratch, interpolating between measures in a musically meaningful way, and manipulating specific musical attributes. We also introduce chord conditioning, which allows all of these operations to be performed while keeping harmony fixed, and allows chords to be changed while maintaining musical "style". By generating a sequence of measures over a predefined chord progression, our model can produce music with convincing long-term structure. We demonstrate that our latent space model makes it possible to intuitively control and generate musical sequences with rich instrumentation (see this https URL for generated audio).
Other recent systems for generating polyphonic music include JamBot @cite_22 which generates chords then notes in a two-step process, DeepJ @cite_32 which generates polyphonic piano music where a user can control several style parameters, a model from @cite_28 that generates variations on lead sheets, and Song from PI @cite_8 which generates melody, chord, and drum tracks using a hierarchical recurrent network combined with hand-engineered features.
@cite_28 @cite_32 @cite_22 @cite_8
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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 proposes a Bayesian approach for automatic music transcription of polyphonic MIDI signals based on generative modeling of onset occurrences of musical notes. Automatic music transcription involves two subproblems that are interdependent of each other: rhythm recognition and tempo estimation. When we listen to music, we are able to recognize its rhythm and tempo (or beat location) fairly easily even though there is ambiguity in determining the individual note values and tempo. This may be made possible through our empirical knowledge about rhythm patterns and tempo variations that possibly occur in music. To automate the process of recognizing the rhythm and tempo of music, we propose modeling the generative process of a MIDI signal of polyphonic music by combining the sub-process by which a musically natural tempo curve is generated and the sub-process by which a set of note onset positions is generated based on a 2-dimensional rhythm tree structure representation of music, and develop a parameter inference algorithm for the proposed model. We show some of the transcription results obtained with the present method. Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instruments tracks with their own temporal dynamics, but collectively they unfold over time interdependently. Lastly, musical notes are often grouped into chords, arpeggios or melodies in polyphonic music, and thereby introducing a chronological ordering of notes is not naturally suitable. In this paper, we propose three models for symbolic multi-track music generation under the framework of generative adversarial networks (GANs). The three models, which differ in the underlying assumptions and accordingly the network architectures, are referred to as the jamming model, the composer model and the hybrid model. We trained the proposed models on a dataset of over one hundred thousand bars of rock music and applied them to generate piano-rolls of five tracks: bass, drums, guitar, piano and strings. A few intra-track and inter-track objective metrics are also proposed to evaluate the generative results, in addition to a subjective user study. We show that our models can generate coherent music of four bars right from scratch (i.e. without human inputs). We also extend our models to human-AI cooperative music generation: given a specific track composed by human, we can generate four additional tracks to accompany it. All code, the dataset and the rendered audio samples are available at this https URL . We describe a neural network architecture which enables prediction and composition of polyphonic music in a manner that preserves translation-invariance of the dataset. Specifically, we demonstrate training a probabilistic model of polyphonic music using a set of parallel, tied-weight recurrent networks, inspired by the structure of convolutional neural networks. This model is designed to be invariant to transpositions, but otherwise is intentionally given minimal information about the musical domain, and tasked with discovering patterns present in the source dataset. We present two versions of the model, denoted TP-LSTM-NADE and BALSTM, and also give methods for training the network and for generating novel music. This approach attains high performance at a musical prediction task and successfully creates note sequences which possess measure-level musical structure.