Applied & Human-Centered · Computer Science & AI
Sound
The newest papers in Sound that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
553 papers, from 4 September 2026 to 5 November 2026: 553 arXiv preprints.
Get the few papers worth your time in Sound every morning.
Follow Sound and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 21
Estimation of Room Impulse Responses from Handclaps
Handclaps provide an equipment-free excitation for room acoustics, but their unknown and variable source waveform makes room impulse response (RIR) estimation challenging.Quieter Than the Room: Representation Drift and Task Robustness in Speech Encoders
Non-speech interference can change a speech representation without causing comparable task loss.Physiologically Informed Digital Auscultation for Pneumonia Detection in Long-term Care Residents
Pneumonia is difficult to diagnose in older long-term care residents; multimorbidity and atypical presentations obscure signs, motivating operationally efficient objective testing.Neither Silence nor Overlap Is Failure: Intent-Conditioned Evaluation of Turn-Taking in Full-Duplex Spoken Dialogue Models
Benchmarks for full-duplex spoken dialogue models score turn-taking with binary fixed-window rules that reward immediate response or silence by completeness of the prior turn.Psychoacoustically Aligned Latent Smoothing for Adversarial Robustness of Full-Duplex Speech-to-Speech Dialogue Models
End-to-end speech-to-speech dialogue models listen and speak simultaneously, so a continuously open acoustic channel is exposed to adversarial manipulation.When Entanglement Lower-Bounds Disparity: Auditing and Repairing Demographic Fairness in Audio Understanding Models
Speech technology penalizes some voices: recognition errs nearly twice as often for Black speakers, and accuracy declines for second-language accents and older speakers.EvoAudio: Recursive Self-Improvement for Audio Understanding
Audio language models understand what is said far better than how it sounds.Forget who you Forgot: Speaker Unlearning to Prevent Re-Identification in Zero-Shot Text-to-Speech
Recent zero-shot text-to-speech (ZS-TTS) systems can reproduce a speaker's voice with high fidelity from only a few seconds of reference speech, raising concerns over unauthorized voice cloning and…Passing: An Endless Journey through Reconstructed Spacetime with AI-Generated Sound
This paper introduces Passing, an interactive audiovisual installation that generates an endless journey from a single continuous monorail-window recording by reconstructing it as a spatiotemporal…BiCFlow-MER: Orchestrating Discriminative and Generative Multimodal Emotion Recognition via Conditional Transport
In multimodal emotion recognition (MER), human affective states are inferred by integrating complementary cues from multiple modalities.Mizar: A 159M-Parameter Audio-Language Model for Audio Understanding
Audio-language models (ALMs) integrate acoustic perception with the knowledge encoded in language models, enabling contextual understanding of auditory events.PTC-Bias: Phoneme-Level Temporal Competition for Bias Retrieval and Post-Decoding Correction in Speech LLMs
Contextual biasing improves rare-word recognition in speech large language models (SpeechLLMs), but efficiently exploiting large bias lists remains challenging.Temporal Taxation Compounds Under Post-Training Compression of Whisper Models
Automatic speech recognition models are audited for demographic fairness at full precision, yet the models that ship to production have been quantized, pruned, and distilled.BiMamba2 Masked Discrete-Unit Prediction for Multilingual Speech Representation for Unsupervised Speech in the Wild Challenge
We describe our submission to the Unsupervised Speech in the Wild (UPS) Challenge at Interspeech 2026, a bidirectional Mamba-2 (BiMamba2) encoder trained with masked discrete-unit prediction…Reward-Tilted On-Policy Distillation for Acoustic Grounding in Audio-Language Models
Audio-language models (ALMs) can exploit textual shortcuts to answer questions while overlooking acoustic evidence, weakening audio understanding.Acoustic domain shift in spoken language identification from systematic domain generalization evaluation to real-world application
Domain Generalization (DG) aims to develop models that remain robust to conditions unseen during training.Synthesis and editing of multi-instrument audio mixtures using scalar-quantised latents with MIDI Span conditioning
Music creation often involves iterative refinement, changing selected musical details while retaining the rest.TV-AudioRemover: Joint Text-Visual Guided Sound Removal with Multi-Task Hard-Mixture Curriculum
Visual object removal can eliminate a target from video frames, yet its acoustic trace persists in the soundtrack, causing obvious audio-visual inconsistency.Challenges of Multi-Speaker Extraction for Real Conversational Speech Enhancement
Target-speaker and multi-speaker extraction are techniques for extracting speech from a desired speaker or desired speakers in the presence of other speakers and/or noise.SE-MSB: End-to-End Unpaired Speech Enhancement using Mamba Schrödinger Bridges
Speech enhancement (SE) models typically rely on supervised learning with paired data examples where clean speech is synthetically degraded.