Search Results for author:
Found 9 papers, 5 papers with code
Nested Music Transformer: Sequentially Decoding Compound Tokens in Symbolic Music and Audio Generation
The NMT consists of two transformers: the main decoder that models a sequence of compound tokens and the sub-decoder for modeling sub-tokens of each compound token.
Six Dragons Fly Again: Reviving 15th-Century Korean Court Music with Transformers and Novel Encoding
The resulting machine-transformed version of Chihwapyeong and Chwipunghyeong were evaluated by experts and performed by the Court Music Orchestra of National Gugak Center.
Towards Efficient and Real-Time Piano Transcription Using Neural Autoregressive Models
To this end, we propose novel architectures for convolutional recurrent neural networks, redesigning an existing autoregressive piano transcription model.
K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling
To broaden the scope of genres and languages in lyric translation studies, we introduce a novel singable lyric translation dataset, approximately 89\% of which consists of K-pop song lyrics.
Finding Tori: Self-supervised Learning for Analyzing Korean Folk Song
In this paper, we introduce a computational analysis of the field recording dataset of approximately 700 hours of Korean folk songs, which were recorded around 1980-90s.
TräumerAI: Dreaming Music with StyleGAN
The goal of this paper to generate a visually appealing video that responds to music with a neural network so that each frame of the video reflects the musical characteristics of the corresponding audio clip.
Ranked #1 on
Music Auto-Tagging
on TimeTravel
(using extra training data)
Polyphonic Piano Transcription Using Autoregressive Multi-State Note Model
Recent advances in polyphonic piano transcription have been made primarily by a deliberate design of neural network architectures that detect different note states such as onset or sustain and model the temporal evolution of the states.
VirtuosoNet: A Hierarchical RNN-based System for Modeling Expressive Piano Performance
In this paper, we present our application of deep neural network to modeling piano performance, which imitates the expressive control of tempo, dynamics, articulations and pedaling from pianists.
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance
Music score is often handled as one-dimensional sequential data.
