Icassp 2023 deep noise suppression challenge
The ICASSP 2023 Deep Noise Suppression (DNS) Challenge marks the fifth edition of the
DNS challenge series. DNS challenges were organized from 2019 to 2023 to foster …
DNS challenge series. DNS challenges were organized from 2019 to 2023 to foster …
Metricgan+: An improved version of metricgan for speech enhancement
The discrepancy between the cost function used for training a speech enhancement model
and human auditory perception usually makes the quality of enhanced speech …
and human auditory perception usually makes the quality of enhanced speech …
DNSMOS: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors
Human subjective evaluation is the" gold standard" to evaluate speech quality optimized for
human perception. Perceptual objective metrics serve as a proxy for subjective scores. The …
human perception. Perceptual objective metrics serve as a proxy for subjective scores. The …
DNSMOS P. 835: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors
Human subjective evaluation is the" gold standard" to evaluate speech quality optimized for
human perception. Perceptual objective metrics serve as a proxy for subjective scores. We …
human perception. Perceptual objective metrics serve as a proxy for subjective scores. We …
Dreamsim: Learning new dimensions of human visual similarity using synthetic data
Current perceptual similarity metrics operate at the level of pixels and patches. These
metrics compare images in terms of their low-level colors and textures, but fail to capture mid …
metrics compare images in terms of their low-level colors and textures, but fail to capture mid …
Taming visually guided sound generation
Recent advances in visually-induced audio generation are based on sampling short, low-
fidelity, and one-class sounds. Moreover, sampling 1 second of audio from the state-of-the …
fidelity, and one-class sounds. Moreover, sampling 1 second of audio from the state-of-the …
CDPAM: Contrastive learning for perceptual audio similarity
Many speech processing methods based on deep learning require an automatic and
differentiable audio metric for the loss function. The DPAM approach of Manocha et al.[1] …
differentiable audio metric for the loss function. The DPAM approach of Manocha et al.[1] …
Synergy between human and machine approaches to sound/scene recognition and processing: An overview of ICASSP special session
Machine Listening, as usually formalized, attempts to perform a task that is, from our
perspective, fundamentally human-performable, and performed by humans. Current …
perspective, fundamentally human-performable, and performed by humans. Current …
HiFi-GAN-2: Studio-quality speech enhancement via generative adversarial networks conditioned on acoustic features
Modern speech content creation tasks such as podcasts, video voice-overs, and audio
books require studio-quality audio with full bandwidth and balanced equalization (EQ) …
books require studio-quality audio with full bandwidth and balanced equalization (EQ) …
Be everywhere-hear everything (bee): Audio scene reconstruction by sparse audio-visual samples
Fully immersive and interactive audio-visual scenes are dynamic such that the listeners and
the sound emitters move and interact with each other. Reconstruction of an immersive sound …
the sound emitters move and interact with each other. Reconstruction of an immersive sound …