Search Results for author:
Found 15 papers, 11 papers with code
Rethinking Emotion Bias in Music via Frechet Audio Distance
In this work, we conduct a study on Music Emotion Recognition (MER) and Emotional Music Generation (EMG), employing diverse audio encoders alongside the Frechet Audio Distance (FAD), a reference-free evaluation metric.
Multi-label audio classification with a noisy zero-shot teacher
We propose a novel training scheme using self-label correction and data augmentation methods designed to deal with noisy labels and improve real-world accuracy on a polyphonic audio content detection task.
Gaussian Flow Bridges for Audio Domain Transfer with Unpaired Data
This paper investigates the potential of Gaussian Flow Bridges, an emerging approach in generative modeling, for this problem.
PAM: Prompting Audio-Language Models for Audio Quality Assessment
Here, we exploit this capability and introduce PAM, a no-reference metric for assessing audio quality for different audio processing tasks.
CMMD: Contrastive Multi-Modal Diffusion for Video-Audio Conditional Modeling
We introduce a multi-modal diffusion model tailored for the bi-directional conditional generation of video and audio.
Adapting Frechet Audio Distance for Generative Music Evaluation
The growing popularity of generative music models underlines the need for perceptually relevant, objective music quality metrics.
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.
Speech MOS multi-task learning and rater bias correction
The mean opinion score (MOS) is standardized for the perceptual evaluation of speech quality and is obtained by asking listeners to rate the quality of a speech sample.
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.
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.
Effect of noise suppression losses on speech distortion and ASR performance
Deep learning based speech enhancement has made rapid development towards improving quality, while models are becoming more compact and usable for real-time on-the-edge inference.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Towards efficient models for real-time deep noise suppression
It is shown that the achievable speech quality is a function of network complexity, and show which models have better tradeoffs.
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.
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
Fast acoustic scattering using convolutional neural networks
Diffracted scattering and occlusion are important acoustic effects in interactive auralization and noise control applications, typically requiring expensive numerical simulation.
