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Found 8 papers, 4 papers with code
Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation
In this work, we replace transformers with Mamba, a selective state space model, for speech separation.
Listen, Chat, and Edit: Text-Guided Soundscape Modification for Enhanced Auditory Experience
In daily life, we encounter a variety of sounds, both desirable and undesirable, with limited control over their presence and volume.
Exploring Self-Supervised Contrastive Learning of Spatial Sound Event Representation
In this study, we present a simple multi-channel framework for contrastive learning (MC-SimCLR) to encode 'what' and 'where' of spatial audios.
HiFTNet: A Fast High-Quality Neural Vocoder with Harmonic-plus-Noise Filter and Inverse Short Time Fourier Transform
Subjective evaluations on LJSpeech show that our model significantly outperforms both iSTFTNet and HiFi-GAN, achieving ground-truth-level performance.
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes
Lifelong audio feature extraction involves learning new sound classes incrementally, which is essential for adapting to new data distributions over time.
Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions
Large-scale pre-trained language models have been shown to be helpful in improving the naturalness of text-to-speech (TTS) models by enabling them to produce more naturalistic prosodic patterns.
Learning Representations for New Sound Classes With Continual Self-Supervised Learning
In this paper, we work on a sound recognition system that continually incorporates new sound classes.
Compute and memory efficient universal sound source separation
Recent progress in audio source separation lead by deep learning has enabled many neural network models to provide robust solutions to this fundamental estimation problem.
Ranked #4 on
Speech Separation
on WHAMR!
