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Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation WhisperPipe: A Resource-Efficient Streaming Architecture for Real-Time Automatic Speech Recognition Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models PSP: An Interpretable Per-Dimension Accent Benchmark for Indic Text-to-Speech Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost 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 RAS: a Reliability Oriented Metric for Automatic Speech Recognition Speech Enhancement Based on Drifting Models HeadRouter: Dynamic Head-Weight Routing for Task-Adaptive Audio Token Pruning in Large Audio Language Models Hallo-Live: Real-Time Streaming Joint Audio-Video Avatar Generation with Asynchronous Dual-Stream and Human-Centric Preference Distillation Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss Spectro-Temporal Modulation Representation Framework for Human-Imitated Speech Detection UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions Do LLM Decoders Listen Fairly? Benchmarking How Language Model Priors Shape Bias in Speech Recognition Materialistic RIR: Material Conditioned Realistic RIR Generation SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation ONOTE: Benchmarking Omnimodal Notation Processing for Expert-level Music Intelligence From Image to Music Language: A Two-Stage Structure Decoding Approach for Complex Polyphonic OMR ATIR: Towards Audio-Text Interleaved Contextual Retrieval Enhancing Speaker Verification with Whispered Speech via Post-Processing Environmental Sound Deepfake Detection Using Deep-Learning Framework Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps Deep Supervised Contrastive Learning of Pitch Contours for Robust Pitch Accent Classification in Seoul Korean HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models UAF: A Unified Audio Front-end LLM for Full-Duplex Speech Interaction Voice of India: A Large-Scale Benchmark for Real-World Speech Recognition in India Tadabur: A Large-Scale Quran Audio Dataset
Noise disturbance and lack of privacy: Modeling acoustic ...
Manuj Yadav, Jungsoo Kim, Valtteri Hongisto, Densil Cabrera, Ric · 2025-01-27 · via cs.SD updates on arXiv.org

Open-plan offices are well-known to be adversely affected by acoustic issues. This study aims to model acoustic dissatisfaction using measurements of room acoustics, sound environment during occupancy, and occupant surveys (n = 349) in 28 offices representing a diverse range of workplace parameters. As latent factors, the contribution of $\textit{lack of privacy}$ (LackPriv) was 25% higher than $\textit{noise disturbance}$ (NseDstrb) in predicting $\textit{acoustic dissatisfaction}$ (AcDsat). Room acoustic metrics based on sound pressure level (SPL) decay of speech ($L_{\text{p,A,s,4m}}$ and $r_{\text{C}}$) were better in predicting these factors than distraction distance ($r_{\text{D}}$) based on speech transmission index. This contradicts previous findings, and the trends for SPL-based metrics in predicting AcDsat and LackPriv go against expectations based on ISO 3382-3. For sound during occupation, $L_{\text{A,90}}$ and psychoacoustic loudness ($N_{\text{90}}$) predicted AcDsat, and a SPL fluctuation metric ($M_{\text{A,eq}}$) predicted LackPriv. However, these metrics were weaker predictors than ISO 3382-3 metrics. Medium-sized offices exhibited higher dissatisfaction than larger ($\geq$50 occupants) offices. Dissatisfaction varied substantially across parameters including ceiling heights, number of workstations, and years of work, but not between offices with fixed seating compared to more flexible and activity-based working configurations. Overall, these findings highlight the complexities in characterizing occupants' perceptions using instrumental acoustic measurements.