Search Results for author:
Found 37 papers, 21 papers with code
A multidimensional measurement of photorealistic avatar quality of experience
We provide an open source test framework to subjectively measure photorealistic avatar performance in ten dimensions: realism, trust, comfortableness using, comfortableness interacting with, appropriateness for work, creepiness, formality, affinity, resemblance to the person, and emotion accuracy.
Topic-Conversation Relevance (TCR) Dataset and Benchmarks
Workplace meetings are vital to organizational collaboration, yet a large percentage of meetings are rated as ineffective.
ICASSP 2024 Speech Signal Improvement Challenge
The ICASSP 2024 Speech Signal Improvement Grand Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems.
Offline to Online Learning for Real-Time Bandwidth Estimation
The cloned policy can then be seamlessly tailored to end user network conditions through online finetuning.
ICASSP 2023 Acoustic Echo Cancellation Challenge
This is the fourth AEC challenge and it is enhanced by adding a second track for personalized acoustic echo cancellation, reducing the algorithmic + buffering latency to 20ms, as well as including a full-band version of AECMOS.
A Real-Time Active Speaker Detection System Integrating an Audio-Visual Signal with a Spatial Querying Mechanism
We introduce a distinctive real-time, causal, neural network-based active speaker detection system optimized for low-power edge computing.
VCD: A Video Conferencing Dataset for Video Compression
We present the Video Conferencing Dataset (VCD) for evaluating video codecs for real-time communication, the first such dataset focused on video conferencing.
Full Reference Video Quality Assessment for Machine Learning-Based Video Codecs
We also propose a new full reference video quality assessment (FRVQA) model that achieves a Pearson Correlation Coefficient (PCC) of 0. 99 and a Spearman's Rank Correlation Coefficient (SRCC) of 0. 99 at the model level.
DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic Echo Cancellation, Noise Suppression and Dereverberation
Acoustic echo cancellation (AEC), noise suppression (NS) and dereverberation (DR) are an integral part of modern full-duplex communication systems.
Improving Meeting Inclusiveness using Speech Interruption Analysis
In order to drive adoption of its usage to improve inclusiveness (and participation), we present a machine learning-based system that predicts when a meeting participant attempts to obtain the floor, but fails to interrupt (termed a `failed interruption').
LSTM-based Video Quality Prediction Accounting for Temporal Distortions in Videoconferencing Calls
Using these features together with VMAF core features, our proposed model achieves a PCC of 0. 99 on the validation set.
ICASSP 2023 Speech Signal Improvement Challenge
The ICASSP 2023 Speech Signal Improvement Challenge is intended to stimulate research in the area of improving the speech signal quality in communication systems.
Real-time Speech Interruption Analysis: From Cloud to Client Deployment
Meetings are an essential form of communication for all types of organizations, and remote collaboration systems have been much more widely used since the COVID-19 pandemic.
A crowdsourcing approach to video quality assessment
P. 910 is slow, expensive, and requires a lab, which all create barriers to usage.
ICASSP 2022 Acoustic Echo Cancellation Challenge
This is the third AEC challenge and it is enhanced by including mobile scenarios, adding speech recognition rate in the challenge goal metrics, and making the default sample rate 48 kHz.
ICASSP 2022 Deep Noise Suppression Challenge
We open-source datasets and test sets for researchers to train their deep noise suppression models, as well as a subjective evaluation framework based on ITU-T P. 835 to rate and rank-order the challenge entries.
Performance optimizations on deep noise suppression models
We study the role of magnitude structured pruning as an architecture search to speed up the inference time of a deep noise suppression (DNS) model.
MusicNet: Compact Convolutional Neural Network for Real-time Background Music Detection
With the recent growth of remote work, online meetings often encounter challenging audio contexts such as background noise, music, and echo.
Aura: Privacy-preserving Augmentation to Improve Test Set Diversity in Speech Enhancement
Noise suppression models running in production environments are commonly trained on publicly available datasets.
AECMOS: A speech quality assessment metric for echo impairment
Traditionally, the quality of acoustic echo cancellers is evaluated using intrusive speech quality assessment measures such as ERLE \cite{g168} and PESQ \cite{p862}, or by carrying out subjective laboratory tests.
DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors
In this work, we train an objective metric based on P. 835 human ratings that outputs 3 scores: i) speech quality (SIG), ii) background noise quality (BAK), and iii) the overall quality (OVRL) of the audio.
Interspeech 2021 Deep Noise Suppression Challenge
In this version of the challenge organized at INTERSPEECH 2021, we are expanding both our training and test datasets to accommodate full band scenarios.
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Large software systems tune hundreds of 'constants' to optimize their runtime performance.
DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors
The no-reference approaches correlate poorly with human ratings and are not widely adopted in the research community.
Subjective Evaluation of Noise Suppression Algorithms in Crowdsourcing
The quality of the speech communication systems, which include noise suppression algorithms, are typically evaluated in laboratory experiments according to the ITU-T Rec.
Crowdsourcing approach for subjective evaluation of echo impairment
The quality of acoustic echo cancellers (AECs) in real-time communication systems is typically evaluated using objective metrics like ERLE and PESQ, and less commonly with lab-based subjective tests like ITU-T Rec.
ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets and Testing Framework
In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios.
Acoustic echo cancellation
Audio and Speech Processing
Sound
Lumos: A Library for Diagnosing Metric Regressions in Web-Scale Applications
Web-scale applications can ship code on a daily to weekly cadence.
An Open source Implementation of ITU-T Recommendation P.808 with Validation
We provide an open-source implementation of the ITU-T Rec.
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results
In this challenge, we open-sourced a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.
Multimodal active speaker detection and virtual cinematography for video conferencing
Active speaker detection (ASD) and virtual cinematography (VC) can significantly improve the remote user experience of a video conference by automatically panning, tilting and zooming of a video conferencing camera: users subjectively rate an expert video cinematographer's video significantly higher than unedited video.
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework
In this challenge, we open-source a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.
Reinforcement learning for bandwidth estimation and congestion control in real-time communications
Bandwidth estimation and congestion control for real-time communications (i. e., audio and video conferencing) remains a difficult problem, despite many years of research.
A scalable noisy speech dataset and online subjective test framework
Our subjective MOS evaluation is the first large scale evaluation of Speech Enhancement algorithms that we are aware of.
Supervised Classifiers for Audio Impairments with Noisy Labels
The user feedback after the call can act as the ground truth labels for training a supervised classifier on a large audio dataset.
On Design of Problem Token Questions in Quality of Experience Surveys
Based on 900, 000 calls gathered using a randomized controlled experiment from a live system, we find that the order bias can be significantly reduced by randomizing the display order of tokens.
