140 papers
This paper introduces TimberAgent, a Gram-guided retrieval system using Texture Resonance Retrieval (TRR) to bridge the semantic gap between user intent and low-level audio effect parameters, demonstrating superior performance in predicting editable plugin configurations through rigorous benchmarking and perceptual evaluation.
This paper analyzes gender bias in audio deepfake detection using the ASVspoof 5 dataset and a ResNet-18 classifier, demonstrating that while aggregate metrics like Equal Error Rate may suggest low disparity, fairness-aware evaluation reveals significant gender-specific error distributions that necessitate more equitable and robust detection systems.
The paper introduces VoxEmo, a comprehensive benchmark and toolkit for evaluating Speech Large Language Models on speech emotion recognition across 35 corpora and 15 languages, featuring a distribution-aware soft-label protocol that reveals how these models uniquely align with human subjective emotion distributions despite trailing supervised baselines in hard-label accuracy.
This paper introduces Fish Audio S2, an open-source text-to-speech system that leverages a multi-stage training pipeline to enable multi-speaker, multi-turn generation with natural-language instruction following, while providing production-ready weights and an efficient SGLang-based inference engine.
The paper introduces EDMFormer, a transformer model trained on a newly released dataset of 98 professionally annotated EDM tracks (EDM-98) to address the limitations of existing music segmentation methods by leveraging genre-specific energy, rhythm, and timbre features for improved structure detection in Electronic Dance Music.
This paper introduces LibriTTS-VI, the first public corpus for numerical voice impression control, and proposes novel training methods to mitigate impression leakage, achieving significantly improved controllability over existing prompt-based approaches.
The paper proposes SUBARU, a power-efficient framework for hearables that intentionally employs sub-Nyquist sampling and low bit-resolution ADCs to achieve a 3.31x reduction in power consumption while maintaining high-quality multimodal speech enhancement through a novel wideband reconstruction methodology.
This paper introduces WaLi, a novel attack framework that leverages standard HVAC pressure sensors to reconstruct intelligible human speech from noisy, low-resolution data using a complex-valued conformer and Complex Global Attention Block, thereby revealing a significant, previously unaddressed privacy vulnerability in building infrastructure.
ExpGest is a novel diffusion-based framework that generates expressive, controllable full-body gestures by leveraging synchronized audio and text guidance, along with a specialized noise emotion classifier, to overcome the limitations of existing methods that often produce stiff, upper-body-only movements.
This paper introduces the Multimodal Laryngoscopic Video Analyzing System (MLVAS), a novel framework that integrates audio keyword spotting with diffusion-refined video segmentation to automatically extract key metrics and features for the objective assisted diagnosis of Vocal Fold Paralysis.
This paper proposes RNDVoC, a scalable and lightweight neural vocoder that bridges classical range-null space decomposition with deep learning to achieve state-of-the-art performance while addressing challenges in model transparency, retraining flexibility, and parameter efficiency.
This paper introduces a systematic framework for Large Audio-Language Models that reformulates ambiguous emotion recognition as a distributional reasoning problem, utilizing an ambiguity-aware objective and structured chain-of-thought supervision to significantly improve performance on standard benchmarks.
This paper introduces ESC, an Evolution Strategy-based calibration method that addresses the unique challenges of audio signal quantization by optimizing activation scaling, thereby achieving near-lossless performance for INT4 and full INT8 quantization across multiple speech tasks.
This paper proposes a novel spectrogram-based Convolutional Neural Network (CNN) approach for multilabel environmental sound classification that significantly outperforms traditional MFCC-based methods on the South Asian SAS-KIIT and UrbanSound8K datasets, offering a more robust solution for complex, overlapping acoustic environments.
This paper introduces PathBench, a unified benchmark for pathological speech intelligibility assessment that establishes systematic baselines across six public datasets and three evaluation protocols, while proposing the Dual-ASR Articulatory Precision (DArtP) method as a top-performing reference-free approach.
This paper introduces WhispEar, a bidirectional framework that leverages a normal-to-whisper model to generate scalable pseudo-parallel data for training a whisper-to-normal conversion system, thereby overcoming data scarcity challenges and achieving superior performance on a newly released bilingual whispered-normal corpus.
This paper introduces MambaDance, a novel dance generation framework that replaces Transformers with a Mamba-based diffusion model and employs a Gaussian-based beat representation to effectively capture the sequential, rhythmic, and music-synchronized nature of dance across varying sequence lengths.
This paper presents a modular, unsupervised domain adaptation pipeline that combines Wav2Vec 2.0 embeddings with statistical transformations like CORAL alignment and feature selection to significantly improve cross-domain generalization for audio deepfake detection without requiring labeled target data.
SoundWeaver is a training-free, model-agnostic serving system that accelerates text-to-audio diffusion by warm-starting generation from semantically similar cached audio, achieving a 1.8–3.0× latency reduction while preserving perceptual quality.
VoiceSHIELD-Small is a lightweight, real-time model built on Whisper-small that simultaneously transcribes speech and detects malicious content with 99.16% accuracy, offering a faster and more secure alternative to traditional text-based filtering for voice AI systems.