The utilization of deep learning techniques in generating various contents (such as image,
text, etc.) has become a trend. Especially music, the topic of this paper, has attracted …

Learning disentangled representations of textual data is essential for many natural language
tasks such as fair classification, style transfer and sentence generation, among others. The …

Many speech processing methods based on deep learning require an automatic and
differentiable audio metric for the loss function. The DPAM approach of Manocha et al.[1] …

High-level musical qualities (such as emotion) are often abstract, subjective, and hard to
quantify. Given these difficulties, it is not easy to learn good feature representations with …

In this paper, we learn disentangled representations of timbre and pitch for musical
instrument sounds. We adapt a framework based on variational autoencoders with Gaussian …

Music creation involves not only composing the different parts (eg, melody, chords) of a
musical work but also arranging/selecting the instruments to play the different parts. While …

Regional style in Chinese folk songs is a rich treasure that can be used for ethnic music
creation and folk culture research. In this paper, we propose MG-VAE, a music generative …

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Disentangling factors of variation aims to uncover latent variables that underlie the process
of data generation. In this paper, we propose a framework that achieves unsupervised pitch …

To achieve a flexible recommendation and retrieval system, it is desirable to calculate music
similarity by focusing on multiple partial elements of musical pieces and allowing the users …