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

arXiv: https://arxiv.org/abs/2607.10345 · HTML: https://mlsanity.com/paper/2607.10345/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 37 references that are in the map.
- First posted on arXiv 11 Jul 2026.

## Builds on (most cited first, 28 of 28)

- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [WHAM!: Extending Speech Separation to Noisy Environments](https://mlsanity.com/paper/1907.01160/) (2019)
- [ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild](https://mlsanity.com/paper/2210.02437/) (2022)
- [ADD 2022: the First Audio Deep Synthesis Detection Challenge](https://mlsanity.com/paper/2202.08433/) (2022)
- [AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Dataset](https://mlsanity.com/paper/2311.15308/) (2023)
- [A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection](https://mlsanity.com/paper/2103.11326/) (2021)
- [The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance](https://mlsanity.com/paper/2204.05177/) (2022)
- [LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation](https://mlsanity.com/paper/2409.14743/) (2024)
- [SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context](https://mlsanity.com/paper/2009.05188/) (2020)
- [The Sound Demixing Challenge 2023 unicodex2013 Cinematic Demixing Track](https://mlsanity.com/paper/2308.06981/) (2023)
- [Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis](https://mlsanity.com/paper/2504.10352/) (2025)
- [PartialEdit: Identifying Partial Deepfakes in the Era of Neural Speech Editing](https://mlsanity.com/paper/2506.02958/) (2025)
- [AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations](https://mlsanity.com/paper/2507.20579/) (2025)
- [Discl-VC: Disentangled Discrete Tokens and In-Context Learning for Controllable Zero-Shot Voice Conversion](https://mlsanity.com/paper/2505.24291/) (2025)
- [CompSpoof: A Dataset and Joint Learning Framework for Component-Level Audio Anti-spoofing Countermeasures](https://mlsanity.com/paper/2509.15804/) (2025)
- [Analyzing the Impact of Splicing Artifacts in Partially Fake Speech Signals](https://mlsanity.com/paper/2408.13784/) (2024)
- [ReFlow-VC: Zero-shot Voice Conversion Based on Rectified Flow and Speaker Feature Optimization](https://mlsanity.com/paper/2506.01032/) (2025)
- [SAO-Instruct: Free-form Audio Editing using Natural Language Instructions](https://mlsanity.com/paper/2510.22795/) (2025)
- [Overview of ESDD2: Environment-Aware Speech and Sound Deepfake Detection Challenge](https://mlsanity.com/paper/2606.10791/) (2026)
- [PhonemeFake: Redefining Deepfake Realism with Language-Driven Segmental Manipulation and Adaptive Bilevel Detection](https://mlsanity.com/paper/2506.22783/) (2025)
- [AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan](https://mlsanity.com/paper/2604.08184/) (2026)
- [Addressing Gradient Misalignment in Data-Augmented Training for Robust Speech Deepfake Detection](https://mlsanity.com/paper/2509.20682/) (2025)
- [The Impact of Audio Watermarking on Audio Anti-Spoofing Countermeasures](https://mlsanity.com/paper/2509.20736/) (2025)
- [MultiAPI Spoof: A Multi-API Dataset and Local-Attention Network for Speech Anti-spoofing Detection](https://mlsanity.com/paper/2512.07352/) (2025)
- [On Deepfake Voice Detection -- It's All in the Presentation](https://mlsanity.com/paper/2509.26471/) (2025)

## Abstract (arXiv)

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.