MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment

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 https://salu133445.github.io/musegan/ .Comment: to appear at AAAI 201

Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting

Recent work has proposed various adversarial loss functions for training either generative or discriminative models. Yet, it remains unclear what certain types of functions are valid adversarial losses, and how these loss functions perform against one another. In this paper, we aim to gain a deeper understanding of adversarial losses by decoupling the effects of their component functions and regularization terms. We first derive in theory some necessary and sufficient conditions of the component functions such that the adversarial loss is a divergence-like measure between the data and the model distributions. In order to systematically compare different adversarial losses, we then propose a new, simple comparative framework, dubbed DANTest, based on discriminative adversarial networks (DANs). With this framework, we evaluate an extensive set of adversarial losses by combining different component functions and regularization approaches. Our theoretical and empirical results can together serve as a reference for choosing or designing adversarial training objectives in future research

Topology optimization of broadband hyperbolic elastic metamaterials with super-resolution imaging

Hyperbolic metamaterials are strongly anisotropic artificial composite materials at a subwavelength scale and can greatly widen the engineering feasibilities for manipulation of wave propagation. However, limited by the empirical structure topologies, the previously reported hyperbolic elastic metamaterials (HEMMs) suffer from the limitations of relatively narrow frequency width, inflexible adjusting operating subwavelength scale and being difficult to further ameliorate imaging resolution. Here, we develop an inverse-design approach for HEMMs by topology optimization based on the effective medium theory. We successfully design two-dimensional broadband HEMMs supporting multipolar resonances, and theoretically demonstrate their deep-subwavelength imagings for longitudinal waves. Under different prescribed subwavelength scales, the optimized HEMMs exhibit broadband negative effective mass densities. Moreover, benefiting from the extreme enhancement of evanescent waves, an optimized HEMM at the ultra-low frequency can yield a super-high imaging resolution (~{\lambda}/64), representing the record in the field of elastic metamaterials. The proposed computational approach can be easily extended to design hyperbolic metamaterials for other wave counterparts. The present research may provide a novel design methodology for exploring the HEMMs based on unrevealed resonances and serve as a useful guide for the ultrasonography and general biomedical applications.Comment: 23 pages, 13 figure