8 citations · 24 across the 10 of their papers we have counts for
10 papers
Spatial Processing Front-End For Distant ASR Exploiting Self-Attention Channel Combinator
Dushyant Sharma, Rong Gong, James Fosburgh +3
We present a novel multi-channel front-end based on channel shortening with theWeighted Prediction Error (WPE) method followed by a fixed MVDR beamformer used in combination with a…
Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition
Rong Gong, Carl Quillen, Dushyant Sharma +3
When a sufficiently large far-field training data is presented, jointly optimizing a multichannel frontend and an end-to-end (E2E) Automatic Speech Recognition (ASR) backend shows…
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
Eduardo Fonseca, Rong Gong, Xavier Serra
In the past, Acoustic Scene Classification systems have been based on hand crafting audio features that are input to a classifier. Nowadays, the common trend is to adopt data drive…
Towards an efficient deep learning model for musical onset detection
Rong Gong, Xavier Serra
In this paper, we propose an efficient and reproducible deep learning model for musical onset detection (MOD). We first review the state-of-the-art deep learning models for MOD, an…
Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions
Rong Gong, Xavier Serra
In this paper, we tackle the singing voice phoneme segmentation problem in the singing training scenario by using language-independent information -- onset and prior coarse duratio…
Identification of potential Music Information Retrieval technologies for computer-aided jingju singing training
Rong Gong, Xavier Serra
Music Information Retrieval (MIR) technologies have been proven useful in assisting western classical singing training. Jingju (also known as Beijing or Peking opera) singing is di…
Creating an A Cappella Singing Audio Dataset for Automatic Jingju Singing Evaluation Research
Rong Gong, Rafael Caro Repetto, Xavier Serra
The data-driven computational research on automatic jingju (also known as Beijing or Peking opera) singing evaluation lacks a suitable and comprehensive a cappella singing audio da…
Audio to score matching by combining phonetic and duration information
Rong Gong, Jordi Pons, Xavier Serra
We approach the singing phrase audio to score matching problem by using phonetic and duration information - with a focus on studying the jingju a cappella singing case. We argue th…
Score-informed syllable segmentation for a cappella singing voice with convolutional neural networks
Jordi Pons, Rong Gong, Xavier Serra
This paper introduces a new score-informed method for the segmentation of jingju a cappella singing phrase into syllables. The proposed method estimates the most likely sequence of…
Timbre Analysis of Music Audio Signals with Convolutional Neural Networks
Jordi Pons, Olga Slizovskaia, Rong Gong +2
The focus of this work is to study how to efficiently tailor Convolutional Neural Networks (CNNs) towards learning timbre representations from log-mel magnitude spectrograms. We fi…