activity
20182023
most citedNeuralogram: A Deep Neural Network Based Representation for Audio Signals

9 citations · 29 across the 20 of their papers we have counts for

collaborators
Showing 2019Show all

6 papers · 1 filter

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.IR2019

Ranking sentences from product description & bullets for better search

Prateek Verma, Aliasgar Kutiyanawala, Ke Shen

Products in an ecommerce catalog contain information-rich fields like description and bullets that can be useful to extract entities (attributes) using NER based systems. However,…

cs.SD2019

Understanding and Classifying Cultural Music Using Melodic Features Case Of Hindustani, Carnatic And Turkish Music

Amruta Vidwans, Prateek Verma, Preeti Rao

We present a melody based classification of musical styles by exploiting the pitch and energy based characteristics derived from the audio signal. Three prominent musical styles we…

cs.CL2019★ 5 cited

End-to-End Spoken Language Translation

Michelle Guo, Albert Haque, Prateek Verma

In this paper, we address the task of spoken language understanding. We present a method for translating spoken sentences from one language into spoken sentences in another languag…

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…

cs.SD2019★ 5 cited

Audio-Linguistic Embeddings for Spoken Sentences

Albert Haque, Michelle Guo, Prateek Verma +1

We propose spoken sentence embeddings which capture both acoustic and linguistic content. While existing works operate at the character, phoneme, or word level, our method learns l…