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Found 17 papers, 8 papers with code
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.
Towards Real-Time Single-Channel Speech Separation in Noisy and Reverberant Environments
While large state-of-the-art DNNs can achieve excellent separation from anechoic mixtures of speech, the main challenge is to create compact and causal models that can separate reverberant mixtures at inference time.
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.
Task splitting for DNN-based acoustic echo and noise removal
Neural networks have led to tremendous performance gains for single-task speech enhancement, such as noise suppression and acoustic echo cancellation (AEC).
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.
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
Unsupervised Speech Enhancement with speech recognition embedding and disentanglement losses
Our results show that the proposed function effectively improves the speech enhancement performance compared to a baseline trained in a supervised way on the noisy VoxCeleb dataset.
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.
Low complexity online convolutional beamforming
Convolutional beamformers integrate the multichannel linear prediction model into beamformers, which provide good performance and optimality for joint dereverberation and noise reduction tasks.
On training targets for noise-robust voice activity detection
The task of voice activity detection (VAD) is an often required module in various speech processing, analysis and classification tasks.
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.
DBNET: DOA-driven beamforming network for end-to-end farfield sound source separation
Many deep learning techniques are available to perform source separation and reduce background noise.
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
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.
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.
