Search Results for author:
Found 9 papers, 5 papers with code
Pictures Of MIDI: Controlled Music Generation via Graphical Prompts for Image-Based Diffusion Inpainting
Recent years have witnessed significant progress in generative models for music, featuring diverse architectures that balance output quality, diversity, speed, and user control.
Operational Latent Spaces
We investigate the construction of latent spaces through self-supervised learning to support semantically meaningful operations.
Fast Timing-Conditioned Latent Audio Diffusion
Generating long-form 44. 1kHz stereo audio from text prompts can be computationally demanding.
Ranked #1 on
Text-to-Music Generation
on MusicCaps
(KL_passt metric)
Leveraging Neural Representations for Audio Manipulation
We investigate applying audio manipulations using pretrained neural network-based autoencoders as an alternative to traditional signal processing methods, since the former may provide greater semantic or perceptual organization.
espiownage: Tracking Transients in Steelpan Drum Strikes Using Surveillance Technology
Besides improvements on previous metric scores by 10% or more, noteworthy in this project are the introduction of a segmentation-regression map for the entire drum surface yielding interference fringe counts comparable to those obtained via object detection, as well as the accelerated workflow for coordinating the data-cleaning-and-model-training feedback loop for rapid iteration allowing this project to be conducted on a timescale of only 18 days.
ConvNets for Counting: Object Detection of Transient Phenomena in Steelpan Drums
We train an object detector built from convolutional neural networks to count interference fringes in elliptical antinode regions in frames of high-speed video recordings of transient oscillations in Caribbean steelpan drums illuminated by electronic speckle pattern interferometry (ESPI).
Exploring Quality and Generalizability in Parameterized Neural Audio Effects
Deep neural networks have shown promise for music audio signal processing applications, often surpassing prior approaches, particularly as end-to-end models in the waveform domain.
SignalTrain: Profiling Audio Compressors with Deep Neural Networks
In this work we present a data-driven approach for predicting the behavior of (i. e., profiling) a given non-linear audio signal processing effect (henceforth "audio effect").
Challenges for an Ontology of Artificial Intelligence
Each of these factors is seen to present a 'moving target' for discussion, which poses a challenge for both technical specialists and non-practitioners of AI systems development (e. g., philosophers and theologians) to speak meaningfully given that the corpus of AI structures and capabilities evolves at a rapid pace.
