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Found 6 papers, 4 papers with code
Source Separation-based Data Augmentation for Improved Joint Beat and Downbeat Tracking
Due to advances in deep learning, the performance of automatic beat and downbeat tracking in musical audio signals has seen great improvement in recent years.
Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs
In this paper, we present a conceptually different approach that explicitly takes into account the type of the tokens, such as note types and metric types.
Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation
Blind music source separation has been a popular and active subject of research in both the music information retrieval and signal processing communities.
Automatic Composition of Guitar Tabs by Transformers and Groove Modeling
Deep learning algorithms are increasingly developed for learning to compose music in the form of MIDI files.
Sound Audio and Speech Processing
Automatic Melody Harmonization with Triad Chords: A Comparative Study
Several prior works have proposed various methods for the task of automatic melody harmonization, in which a model aims to generate a sequence of chords to serve as the harmonic accompaniment of a given multiple-bar melody sequence.
MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
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.
