9 citations · 11 across the 6 of their papers we have counts for
7 papers
Geometric Signatures of Conceptual Reorganization: A Counterfactual Embedding Framework for Detecting Scientific Revolutions
Dimitris Ntounis, Ariel Schwartzman, Chris Chafe +1
We introduce document embedding geometry as a quantitative observable of conceptual reorganization and develop a counterfactual ablation framework for measuring how individual conc…
Perceiving Slope and Acceleration: Evidence for Variable Tempo Sampling in Pitch-Based Sonification of Functions
Danyang Fan, Walker Smith, Takako Fujioka +4
Sonification offers a non-visual way to understand data, with pitch-based encodings being the most common. Yet, how well people perceive slope and acceleration-key features of data…
Content Adaptive Front End For Audio Classification
Prateek Verma, Chris Chafe
We propose a learnable content adaptive front end for audio signal processing. Before the modern advent of deep learning, we used fixed representation non-learnable front-ends like…
One-Shot Acoustic Matching Of Audio Signals -- Learning to Hear Music In Any Room/ Concert Hall
Prateek Verma, Chris Chafe, Jonathan Berger
The acoustic space in which a sound is created and heard plays an essential role in how that sound is perceived by affording a unique sense of \textit{presence}. Every sound we hea…
A Generative Model for Raw Audio Using Transformer Architectures
Prateek Verma, Chris Chafe
This paper proposes a novel way of doing audio synthesis at the waveform level using Transformer architectures. We propose a deep neural network for generating waveforms, similar t…
A Deep Learning Approach for Low-Latency Packet Loss Concealment of Audio Signals in Networked Music Performance Applications
Prateek Verma, Alessandro Ilic Mezza, Chris Chafe +1
Networked Music Performance (NMP) is envisioned as a potential game changer among Internet applications: it aims at revolutionizing the traditional concept of musical interaction b…
Neuralogram: A Deep Neural Network Based Representation for Audio Signals
Prateek Verma, Chris Chafe, Jonathan Berger
We propose the Neuralogram -- a deep neural network based representation for understanding audio signals which, as the name suggests, transforms an audio signal to a dense, compact…