9 citations · 27 across the 14 of their papers we have counts for
14 papers · 1 filter
Diverse Audio Embeddings -- Bringing Features Back Outperforms CLAP!
Prateek Verma
With the advent of modern AI architectures, a shift has happened towards end-to-end architectures. This pivot has led to neural architectures being trained without domain-specific…
Neural Architectures Learning Fourier Transforms, Signal Processing and Much More....
Prateek Verma
This report will explore and answer fundamental questions about taking Fourier Transforms and tying it with recent advances in AI and neural architecture. One interpretation of the…
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 Framework for Generative and Contrastive Learning of Audio Representations
Prateek Verma, Julius Smith
In this paper, we present a framework for contrastive learning for audio representations, in a self supervised frame work without access to any ground truth labels. The core idea i…