PC-Mix: Partial-Component Audio Spoofing Detection under Mixed Speech and Environmental Sound Conditions

• URL: http://arxiv.org/abs/2607.10345v1
• Date: Sat, 11 Jul 2026 15:12:23 GMT
• Title: PC-Mix: Partial-Component Audio Spoofing Detection under Mixed Speech and Environmental Sound Conditions
• Abstract summary: PC-Mix is the first dataset for partial-component spoofing detection.<n>In PC-Mix, bonafide and partially spoofed environmental-sound components are first constructed and mixed with speech signals.
• Score: 5.865918439276438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies on partial audio spoofing mainly focus on studio-recorded speech with temporal localization of spoofed segments. However, these studies often overlook realistic conditions where spoofed and bonafide segments simultaneously coexist across speech and environmental sound components. In this paper, we present PC-Mix, the first dataset for partial-component spoofing detection, where either or both audio components may be partially spoofed. In PC-Mix, bonafide and partially spoofed environmental-sound components are first constructed and mixed with speech signals from an existing partial-spoof dataset, producing audio in which either or both components may be locally manipulated. This design addresses two major gaps in existing partial spoofing benchmarks: the lack of realistic environmental sounds in speech partial spoofing scenarios and the absence of partial spoofing detection for environmental sound components. We further establish standardized evaluation protocols and design a joint learning framework to optimize spoofing detection across speech, environmental sound, and mixed audio. Experiments highlight the increased difficulty introduced by mixed conditions. The results demonstrate that training under matched target conditions is more effective than directly transferring models trained on speech or environmental sound components.

Related papers

• MADBench: A Benchmark for Modality-Aware Audio Deepfake Detection [18.1593708805924]
  We introduce MADBench, the first benchmark that treats speech and environmental audio as distinct acoustic components.<n>We benchmark representative state-of-the-art detectors and multimodal large language models under a unified protocol.<n>Our experiments reveal that environmental audio manipulation is more detectable than synthetic speech across general-purpose encoders.
  arXiv  Detail & Related papers  (2026-08-10T13:27:09Z)
• Deepfake Audio Detection Using Self-supervised Fusion Representations [5.9998333708296245]
  This paper describes a submission to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) 2026.<n>A dual-branch deepfake detection framework is proposed to jointly model speech and environmental contextual representations from input audio.<n>The proposed system achieves an F1-score of 70.20% and an environmental EER of 16.54%, outperforming the baseline system.
  arXiv  Detail & Related papers  (2026-05-05T06:51:41Z)
• SSNAPS: Audio-Visual Separation of Speech and Background Noise with Diffusion Inverse Sampling [23.130313134690443]
  This paper addresses the challenge of audio-visual single-microphone speech separation and enhancement in the presence of real-world environmental noise.<n>Our approach is based on generative inverse sampling, where we model clean speech and ambient noise with dedicated diffusion priors and jointly leverage them to recover all underlying sources.<n>We evaluate on mixtures of 1, 2, and 3 speakers with noise and show that, despite being entirely unsupervised, our method consistently outperforms leading supervised baselines in acWER across all conditions.
  arXiv  Detail & Related papers  (2026-02-01T18:57:53Z)
• Defense Against Synthetic Speech: Real-Time Detection of RVC Voice Conversion Attacks [0.0]
  Generative audio technologies now enable highly realistic voice cloning and real-time voice conversion.<n>This study investigates real-time detection of AI-generated speech produced using Retrieval-based Voice Conversion.
  arXiv  Detail & Related papers  (2025-12-31T02:06:42Z)
• Diffusion-Based Unsupervised Audio-Visual Speech Separation in Noisy Environments with Noise Prior [24.815262863931334]
  We propose a generative unsupervised technique that models both clean speech and structured noise components.<n>Our approach leverages an audio-visual score model that incorporates visual cues to serve as a strong generative speech prior.<n> Experimental results demonstrate promising performance, highlighting the effectiveness of our direct noise modelling approach.
  arXiv  Detail & Related papers  (2025-09-17T19:25:35Z)
• Unleashing the Power of Natural Audio Featuring Multiple Sound Sources [54.38251699625379]
  Universal sound separation aims to extract clean audio tracks corresponding to distinct events from mixed audio.<n>We propose ClearSep, a framework that employs a data engine to decompose complex naturally mixed audio into multiple independent tracks.<n>In experiments, ClearSep achieves state-of-the-art performance across multiple sound separation tasks.
  arXiv  Detail & Related papers  (2025-04-24T17:58:21Z)
• Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment [28.491885755907575]
  Speech emotion recognition (SER) systems often struggle in real-world environments, where ambient noise severely degrades their performance.<n>This paper explores a novel approach that exploits prior knowledge of testing environments to maximize SER performance under noisy conditions.
  arXiv  Detail & Related papers  (2024-07-25T02:30:40Z)
• How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio? [53.58852794805362]
  countermeasures (CMs) trained on partially spoofed audio can effectively detect such spoofing. We utilize Grad-CAM and introduce a quantitative analysis metric to interpret CMs' decisions. We find that CMs prioritize the artifacts of transition regions created when concatenating bona fide and spoofed audio.
  arXiv  Detail & Related papers  (2024-06-04T16:51:42Z)
• Self-Supervised Visual Acoustic Matching [63.492168778869726]
  Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a target acoustic environment. We propose a self-supervised approach to visual acoustic matching where training samples include only the target scene image and audio. Our approach jointly learns to disentangle room acoustics and re-synthesize audio into the target environment, via a conditional GAN framework and a novel metric.
  arXiv  Detail & Related papers  (2023-07-27T17:59:59Z)
• Sources of Noise in Dialogue and How to Deal with Them [63.02707014103651]
  Training dialogue systems often entails dealing with noisy training examples and unexpected user inputs. Despite their prevalence, there currently lacks an accurate survey of dialogue noise. This paper addresses this gap by first constructing a taxonomy of noise encountered by dialogue systems.
  arXiv  Detail & Related papers  (2022-12-06T04:36:32Z)
• End-to-End Binaural Speech Synthesis [71.1869877389535]
  We present an end-to-end speech synthesis system that combines a low-bitrate audio system with a powerful decoder. We demonstrate the capability of the adversarial loss in capturing environment effects needed to create an authentic auditory scene.
  arXiv  Detail & Related papers  (2022-07-08T05:18:36Z)
• Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification [61.54074498090374]
  This study introduces a CRSS-Forensics audio dataset collected in multiple acoustic environments. We pre-train a CNN-based network using the VoxCeleb data, followed by an approach which fine-tunes part of the high-level network layers with clean speech from CRSS-Forensics.
  arXiv  Detail & Related papers  (2020-09-05T02:54:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.