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Found 14 papers, 6 papers with code
Weakly-supervised Audio Separation via Bi-modal Semantic Similarity
Conditional sound separation in multi-source audio mixtures without having access to single source sound data during training is a long standing challenge.
uaMix-MAE: Efficient Tuning of Pretrained Audio Transformers with Unsupervised Audio Mixtures
Masked Autoencoders (MAEs) learn rich low-level representations from unlabeled data but require substantial labeled data to effectively adapt to downstream tasks.
Learned Image Compression with Text Quality Enhancement
Learned image compression has gained widespread popularity for their efficiency in achieving ultra-low bit-rates.
Single-channel speech enhancement using learnable loss mixup
Generalization remains a major problem in supervised learning of single-channel speech enhancement.
Automatic Disfluency Detection from Untranscribed Speech
We find that disfluency detection performance is largely limited by the quality of transcripts and alignments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
Diffusion models power a vast majority of text-to-audio (TTA) generation methods.
Ranked #10 on
Audio Generation
on AudioCaps
SCP-GAN: Self-Correcting Discriminator Optimization for Training Consistency Preserving Metric GAN on Speech Enhancement Tasks
In recent years, Generative Adversarial Networks (GANs) have produced significantly improved results in speech enhancement (SE) tasks.
Ranked #2 on
Speech Enhancement
on VoiceBank + DEMAND
Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations
Improving generalization is a major challenge in audio classification due to labeled data scarcity.
A Training Framework for Stereo-Aware Speech Enhancement using Deep Neural Networks
Deep learning-based speech enhancement has shown unprecedented performance in recent years.
Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features
Unsupervised Zero-Shot Voice Conversion (VC) aims to modify the speaker characteristic of an utterance to match an unseen target speaker without relying on parallel training data.
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.
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"
To address this, we propose (1) a framework to isolate and evaluate the reasoning aspect of VQA separately from its perception, and (2) a novel top-down calibration technique that allows the model to answer reasoning questions even with imperfect perception.
MMTM: Multimodal Transfer Module for CNN Fusion
In late fusion, each modality is processed in a separate unimodal Convolutional Neural Network (CNN) stream and the scores of each modality are fused at the end.
Ranked #3 on
Hand Gesture Recognition
on NVGesture
Sound Event Detection in Multichannel Audio using Convolutional Time-Frequency-Channel Squeeze and Excitation
In this study, we introduce a convolutional time-frequency-channel "Squeeze and Excitation" (tfc-SE) module to explicitly model inter-dependencies between the time-frequency domain and multiple channels.
