Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.00754

MAV-C: A Framework for the Joint Objective Estimation of Audio-Visual Complexity in Immersive Virtual Environments

Luca Resti·Amelia Gully·Michael McLoughlin·Gavin Kearney·Alena Denisova
View original source ↗Read source document ↗Export metadata ↓

Abstract

We present the Motion-Aware Audio-Visual Complexity Metric (MAV-C), a reference-free framework for the joint objective estimation of audio-visual complexity. The metric combines entropy-based audio features (temporal, spectral, and spatial) with visual features (Sobel gradient magnitude, chromatic uniqueness, and optical flow) via a parametric fusion stage, producing a continuous joint complexity score CAV (t) [0,1]. We validate MAV-C on two datasets: a controlled synthetic corpus (SYN) of stimuli with known signal characteristics and a naturalistic gameplay corpus (GAM) of 60 clips drawn from the SAFEPLAY-X dataset. On SYN, the metric exhibits strong validity: the audio score CA and visual score CV are each insensitive to changes in the opposite modality (CoV < 0.003), the joint score CAV spans [0.00,0.90] across all parameter combinations, and single-axis feature sweeps produce monotone trajectories (Spearman up to 0.995). On GAM, CV differs significantly across content categories (Kruskal-Wallis p = 0.021) while CA does not, and the two sub-scores are uncorrelated (r = 0.03), confirming they operate on independent signal dimensions. OFAT sensitivity analysis identifies a two-tier parameter hierarchy, with modality balance (wa) and visual regularization (v) as most significant tunable parameters. Full subjective calibration is planned as future work.

Explore related subjects

eess.AScs.SDeess.IVeess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Luca Resti, Amelia Gully, Michael McLoughlin, Gavin Kearney, Alena Denisova. 2026-09-30. MAV-C: A Framework for the Joint Objective Estimation of Audio-Visual Complexity in Immersive Virtual Environments. https://arxiv.org/abs/2610.00754

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

ParaCalib: Semantically Calibrated Paralinguistic Modeling for Depression Detection

Vocal behavior provides important signals for speech-based depression detection, but its interpretation often depends on what is being said and how it functions in context. However, existing methods typically treat acoustic cues as context-independent markers, making it difficult to distinguish vocal form from its context-dependent communicative function. We propose ParaCalib, a framework that semantically calibrates paralinguistic behavior by interpreting vocal patterns relative to utterance-level semantic context and inferred communicative function and representing them in a comparable state space. Concretely, ParaCalib uses an Audio-Language Model (ALM) to generate contextualized vocal descriptions and an LLM-based Paralinguistic State Extractor (PSE) to map these descriptions into a structured Semantically Calibrated Paralinguistic (SC-Para) representation. ParaCalib achieves the highest mean Macro-F1 among the evaluated methods, reaching 71.9\% on DAIC-WOZ and 90.5\% on MODMA. Our controlled analysis provides direct evidence of semantic calibration at the PSE stage: under a fixed caption-level acoustic description, varying the accompanying semantic context changes the inferred depression-related paralinguistic evidence. Our exploratory analysis further identifies recurring configurations of paralinguistic states associated with depression labels rather than a single uniformly dominant state.

eess.AS↗
arXiv2026

FedCFM: Federated Continual Domain Generalization for Fake Speech Detection via Conditional Flow Matching

The generalization ability of Fake Speech Detection (FSD) models is crucial for real-world deployment. Existing multi-dataset co-training methods rely on fixed training sets and cannot adapt to emerging spoofing types. Although con-tinual learning has been explored, many approaches overlook limited data storage at individual devices, thereby restricting practical applicability. To address this, we propose FedCFM, a Federated continual domain generalization framework via Conditional Flow Matching (CFM) for collaboration without sharing raw speech data across distributed clients facing diverse and evolving spoofing attacks. Each client trains a CFM-based generator to model spoof-type-specific embedding distributions, and cross-client generator exchange enables synthesis of unseen spoof-type embeddings for continual classifier updating through generative replay and knowledge distillation. With the same training datasets, FedCFM achieves lower EER than the eval-uated centralized and federated domain generalization baselines, demonstrating strong cross-domain generalization. Code will be released on https://github.com/jspycpp/FedCFM.

eess.AS↗
arXiv2026

A Federated Deepfake Speech Detection Method Based on Layer-Wise Center-Guided Weighting Aggregation

The advancement of deep learning-based speech synthesis has significantly increased the diversity of deepfake speech, posing threats to voice authentication. While centralized training is effective for deepfake speech detection (DSD), it requires considerable computational resources and raises privacy concerns. To address these issues, we propose a Federated DSD (FedDSD) method that enables collaborative model training across decentralized speech datasets without sharing raw audio. Specifically, each client trains a local model using the FedProx algorithm to mitigate the effects of data heterogeneity and uploads model parameters to a central server. To improve global model aggregation, we further propose a layer-wise center-guided weighting aggregation (L-CGWA) strategy that adjusts each client's contribution per layer based on its distance to a reference center, capturing inter-client and inter-layer discrepancies and enhancing the robustness of model aggregation. Experimental results demonstrate that models trained under the proposed FedDSD method achieve equal error rates (EERs) comparable to those obtained via centralized co-training, while significantly out-performing models trained on individual corpora. Furthermore, the proposed FedDSD method demonstrates robust generalization capabilities across diverse cross-domain datasets.

eess.AS↗