activity
20192022
most citedAudio Transformers

34 citations · 44 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SD2022

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…

cs.SD2022

The Role of Vocal Persona in Natural and Synthesized Speech

Camille Noufi, Lloyd May, Jonathan Berger

The inclusion of voice persona in synthesized voice can be significant in a broad range of human-computer-interaction (HCI) applications, including augmentative and assistive commu…

eess.AS2022

Acoustically-Driven Phoneme Removal That Preserves Vocal Affect Cues

Camille Noufi, Jonathan Berger, Karen J. Parker +1

In this paper, we propose a method for removing linguistic information from speech for the purpose of isolating paralinguistic indicators of affect. The immediate utility of this m…

cs.SD2022

Enhancing Audio Perception of Music By AI Picked Room Acoustics

Prateek Verma, Jonathan Berger

Every sound that we hear is the result of successive convolutional operations (e.g. room acoustics, microphone characteristics, resonant properties of the instrument itself, not to…

cs.SD2021★ 34 cited

Audio Transformers

Prateek Verma, Jonathan Berger

Over the past two decades, CNN architectures have produced compelling models of sound perception and cognition, learning hierarchical organizations of features. Analogous to succes…

cs.SD2019★ 1 cited

Learning to Model Aspects of Hearing Perception Using Neural Loss Functions

Prateek Verma, Jonathan Berger

We present a framework to model the perceived quality of audio signals by combining convolutional architectures, with ideas from classical signal processing, and describe an approa…

cs.SD2019★ 9 cited

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…