8 citations · 14 across the 3 of their papers we have counts for
3 papers
The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data
Alice Baird, Rachel Manzelli, Panagiotis Tzirakis +7
The NeurIPS 2023 Machine Learning for Audio Workshop brings together machine learning (ML) experts from various audio domains. There are several valuable audio-driven ML tasks, fro…
Deep Divergence Learning
Kubra Cilingir, Rachel Manzelli, Brian Kulis
Classical linear metric learning methods have recently been extended along two distinct lines: deep metric learning methods for learning embeddings of the data using neural network…
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music
Rachel Manzelli, Vijay Thakkar, Ali Siahkamari +1
Existing automatic music generation approaches that feature deep learning can be broadly classified into two types: raw audio models and symbolic models. Symbolic models, which tra…