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eess.AS updates on arXiv.org

Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling Predictive-Generative Drift Decomposition for Speech Enhancement and Separation Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models Mitigating Multimodal LLMs Hallucinations via Relevance Propagation at Inference Time Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapy LASE: Language-Adversarial Speaker Encoding for Indic Cross-Script Identity Preservation Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation Alethia: A Foundational Encoder for Voice Deepfakes From Birdsong to Rumbles: Classifying Elephant Calls with Out-of-Species Embeddings Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification Predicting Upcoming Stuttering Events from Three-Second Audio: Stratified Evaluation Reveals Severity-Selective Precursors, and the Model Deploys Fully On-Device The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation DiffAnon: Diffusion-based Prosody Control for Voice Anonymization Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech Similarity Choice and Negative Scaling in Supervised Contrastive Learning for Deepfake Audio Detection Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost ML-SAN: Multi-Level Speaker-Adaptive Network for Emotion Recognition in Conversations Korean aegyo speech shows systematic F1 increase to signal childlike qualities All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation Speech Enhancement Based on Drifting Models Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling Explainable AI in Speaker Recognition -- Making Latent Representations Understandable TTS-PRISM: A Perceptual Reasoning and Interpretable Speech Model for Fine-Grained Diagnosis UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions Dilated CNNs for Periodic Signal Processing: A Low-Complexity Approach ONOTE: Benchmarking Omnimodal Notation Processing for Expert-level Music Intelligence MoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis Voice Mapping of Text-to-Speech Systems: A Metric-Based Approach for Voice Quality Assessment 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of Dysarthric Speech Interpreting Speaker Characteristics in the Dimensions of Self-Supervised Speech Features From Black Box to Glass Box: Cross-Model ASR Disagreement to Prioto Review in Ambient AI Scribe Documentation WST-X Series: Wavelet Scattering Transform for Interpretable Speech Deepfake Detection Kanade: A Simple Disentangled Tokenizer for Spoken Language Modeling AQUA-Bench: Beyond Finding Answers to Knowing When There Are None in Audio Question Answering Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Models BERT-APC: A Reference-free Framework for Automatic Pitch Correction via Musical Context Inference MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models Sound Source Localization for Spatial Mapping of Surgical Actions in Dynamic Scenes Gelina: Unified Speech and Gesture Synthesis via Interleaved Token Prediction Efficient Test-Time Adaptation through Latent 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Acoustic source depth estimation method based on a single hydrophone in Arctic underwater
2025-08-10 · via eess.AS updates on arXiv.org

Based on the normal mode and ray theory, this article discusses the characteristics of surface sound source and reception at the surface layer, and explores depth estimation methods based on normal modes and rays, and proposes a depth estimation method based on the upper limit of modal frequency. Data verification is conducted to discuss the applicability and limitations of different methods. For the surface refracted normal mode waveguide, modes can be separated through warping transformation. Based on the characteristics of normal mode amplitude variation with frequency and number, the sound source depth can be estimated by matching amplitude information. Based on the spatial variation characteristics of eigenfunctions with frequency, a sound source depth estimation method matching the cutoff frequency of normal modes is proposed. For the deep Arctic sea, the sound ray arrival structure at the receiving end is obtained through the analysis of deep inversion sound ray trajectories, and the sound source depth can be estimated by matching the time difference of ray arrivals. Experimental data is used to verify the sound field patterns and the effectiveness of the sound source depth estimation method.