๐ฎ
๐ฎ
The Ethereal
Current Trends and Future Research Directions for Interactive Music
October 05, 2018 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Mauricio Toro
arXiv ID
1810.04276
Category
cs.SD: Sound
Cross-listed
cs.AI,
eess.AS
Citations
9
Venue
arXiv.org
Last Checked
3 months ago
Abstract
In this review, it is explained and compared different software and formalisms used in music interaction: sequencers, computer-assisted improvisation, meta- instruments, score-following, asynchronous dataflow languages, synchronous dataflow languages, process calculi, temporal constraints and interactive scores. Formal approaches have the advantage of providing rigorous semantics of the behavior of the model and proving correctness during execution. The main disadvantage of formal approaches is lack of commercial tools.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Sound
R.I.P.
๐ป
Ghosted
Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
R.I.P.
๐ป
Ghosted
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
R.I.P.
๐ป
Ghosted
TasNet: time-domain audio separation network for real-time, single-channel speech separation
R.I.P.
๐ป
Ghosted
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
R.I.P.
๐ป
Ghosted
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted