Search Results for author:
Found 6 papers, 3 papers with code
LOAF-M2L: Joint Learning of Wording and Formatting for Singable Melody-to-Lyric Generation
Despite previous efforts in melody-to-lyric generation research, there is still a significant compatibility gap between generated lyrics and melodies, negatively impacting the singability of the outputs.
Songs Across Borders: Singable and Controllable Neural Lyric Translation
The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations.
Transfer Learning of wav2vec 2.0 for Automatic Lyric Transcription
To fill in the performance gap between ALT and ASR, we attempt to exploit the similarities between speech and singing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
MM-ALT: A Multimodal Automatic Lyric Transcription System
Automatic lyric transcription (ALT) is a nascent field of study attracting increasing interest from both the speech and music information retrieval communities, given its significant application potential.
Exploring Transformer's potential on automatic piano transcription
Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation.
Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters
However, most machine learning algorithms are sensitive to the hyper-parameters.
