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Found 12 papers, 5 papers with code
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.
Contextual Feature Extraction Hierarchies Converge in Large Language Models and the Brain
We also compare the feature extraction pathways of the LLMs to each other and identify new ways in which high-performing models have converged toward similar hierarchical processing mechanisms.
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.
SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs
In recent years, large-scale pre-trained speech language models (SLMs) have demonstrated remarkable advancements in various generative speech modeling applications, such as text-to-speech synthesis, voice conversion, and speech enhancement.
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models
In this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis.
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.
Improved Decoding of Attentional Selection in Multi-Talker Environments with Self-Supervised Learned Speech Representation
Auditory attention decoding (AAD) is a technique used to identify and amplify the talker that a listener is focused on in a noisy environment.
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.
StyleTTS-VC: One-Shot Voice Conversion by Knowledge Transfer from Style-Based TTS Models
Here, we propose a novel approach to learning disentangled speech representation by transfer learning from style-based text-to-speech (TTS) models.
StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesis
Text-to-Speech (TTS) has recently seen great progress in synthesizing high-quality speech owing to the rapid development of parallel TTS systems, but producing speech with naturalistic prosodic variations, speaking styles and emotional tones remains challenging.
StarGANv2-VC: A Diverse, Unsupervised, Non-parallel Framework for Natural-Sounding Voice Conversion
We present an unsupervised non-parallel many-to-many voice conversion (VC) method using a generative adversarial network (GAN) called StarGAN v2.
