REVIEW 5 cited by
A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
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 widespread attention of countless researchers.The whole process of producing music can be divided into three stages, corresponding to the three levels of music generation: score generation produces scores, performance generation adds performance characteristics to the scores, and audio generation converts scores with performance characteristics into audio by assigning timbre or generates music in audio format directly. Previous surveys have explored the network models employed in the field of automatic music generation. However, the development history, the model evolution, as well as the pros and cons of same music generation task have not been clearly illustrated. This paper attempts to provide an overview of various composition tasks under different music generation levels, covering most of the currently popular music generation tasks using deep learning. In addition, we summarize the datasets suitable for diverse tasks, discuss the music representations, the evaluation methods as well as the challenges under different levels, and finally point out several future directions.
Forward citations
Cited by 5 Pith papers
-
GVMGen: A General Video-to-Music Generation Model with Hierarchical Attentions
GVMGen generates background music from video using spatial and temporal cross-attention to condition a MusicGen decoder, reporting state-of-the-art correspondence and diversity.
-
AffectMachine-Pop: A controllable expert system for real-time pop music generation
A rule-based system generates retro-pop music at target levels of arousal and valence, validated by a listening study with high correspondence between target and perceived ratings.
-
Velocity Completion Task and Method for Event-based Player Positional Data in Soccer
A graph recurrent neural network can estimate player velocities from event-time positions in soccer, outperforming a rule-based baseline and yielding more accurate pitch control and off-ball scoring opportunity values.
-
Text2midi-InferAlign: Improving Symbolic Music Generation with Inference-Time Alignment
By mutating captions and re-ranking MIDI tokens with CLAP and key-consistency scores, the method improves caption agreement and key matching in text-to-MIDI generation at inference time.
-
Improving Controllability and Editability for Pretrained Text-to-Music Generation Models
A thesis compilation presenting three complementary approaches to improving editing and control of pretrained text-to-music models, with Instruct-MusicGen demonstrating the strongest stem-level editing results.
Discussion (0). Continue with ORCID to comment.