Tootfinder

Content Anonymization for Privacy in Long-form Audio
Cristina Aggazzotti, Ashi Garg, Zexin Cai, Nicholas Andrews
https://arxiv.org/abs/2510.12780 https://a…

Content Anonymization for Privacy in Long-form Audio
Voice anonymization techniques have been found to successfully obscure a speaker's acoustic identity in short, isolated utterances in benchmarks such as the VoicePrivacy Challenge. In practice, however, utterances seldom occur in isolation: long-form audio is commonplace in domains such as interviews, phone calls, and meetings. In these cases, many utterances from the same speaker are available, which pose a significantly greater privacy risk: given multiple utterances from the same speaker, an…

Target speaker anonymization in multi-speaker recordings
Natalia Tomashenko, Junichi Yamagishi, Xin Wang, Yun Liu, Emmanuel Vincent
https://arxiv.org/abs/2510.09307 https://

Target speaker anonymization in multi-speaker recordings
Most of the existing speaker anonymization research has focused on single-speaker audio, leading to the development of techniques and evaluation metrics optimized for such condition. This study addresses the significant challenge of speaker anonymization within multi-speaker conversational audio, specifically when only a single target speaker needs to be anonymized. This scenario is highly relevant in contexts like call centers, where customer privacy necessitates anonymizing only the customer'…

QPADL: Post-Quantum Private Spectrum Access with Verified Location and DoS Resilience
Saleh Darzi, Saif Eddine Nouma, Kiarash Sedghighadikolaei, Attila Altay
https://arxiv.org/abs/2510.03631

QPADL: Post-Quantum Private Spectrum Access with Verified Location and DoS Resilience
With advances in wireless communication and growing spectrum scarcity, Spectrum Access Systems (SASs) offer an opportunistic solution but face significant security challenges. Regulations require disclosure of location coordinates and transmission details, exposing user privacy and anonymity during spectrum queries, while the database operations themselves permit Denial-of-Service (DoS) attacks. As location-based services, SAS is also vulnerable to compromised or malicious users conducting spoo…

Single-Round Deterministic Quantum Anonymous Veto Using Bell States
Ravi Sangwan, Harishankar Mishra, Henry Sukumar, Gudapati Naresh Raghava
https://arxiv.org/abs/2509.15951 htt…

Single-Round Deterministic Quantum Anonymous Veto Using Bell States
Quantum Anonymous Veto (QAV) protocols enable secure and anonymous decision-making by allowing participants to detect the presence of a veto without revealing individual choices. While existing QAV schemes offer strong theoretical guarantees, they face significant limitations in practical implementation due to resource requirements, scalability issues, and the need for multipartite entanglement. In this work, we propose a novel deterministic QAV protocol that leverages only bipartite entangleme…

Scaling Spoken Language Models with Syllabic Speech Tokenization
Nicholas Lee, Cheol Jun Cho, Alan W Black, Gopala K. Anumanchipalli
https://arxiv.org/abs/2509.26634 https://

Scaling Spoken Language Models with Syllabic Speech Tokenization
Spoken language models (SLMs) typically discretize speech into high-frame-rate tokens extracted from SSL speech models. As the most successful LMs are based on the Transformer architecture, processing these long token streams with self-attention is expensive, as attention scales quadratically with sequence length. A recent SSL work introduces acoustic tokenization of speech at the syllable level, which is more interpretable and potentially more scalable with significant compression in token len…

TART: A Comprehensive Tool for Technique-Aware Audio-to-Tab Guitar Transcription
Akshaj Gupta, Andrea Guzman, Anagha Badriprasad, Hwi Joo Park, Upasana Puranik, Robin Netzorg, Jiachen Lian, Gopala Krishna Anumanchipalli
https://arxiv.org/abs/2510.02597

TART: A Comprehensive Tool for Technique-Aware Audio-to-Tab Guitar Transcription
Automatic Music Transcription (AMT) has advanced significantly for the piano, but transcription for the guitar remains limited due to several key challenges. Existing systems fail to detect and annotate expressive techniques (e.g., slides, bends, percussive hits) and incorrectly map notes to the wrong string and fret combination in the generated tablature. Furthermore, prior models are typically trained on small, isolated datasets, limiting their generalizability to real-world guitar recordings…

SynBench: A Benchmark for Differentially Private Text Generation
Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar, Iqra Zahid, Yuping Wu, Yulong Wu, Hao Li, Jie Zhang, Warren Del-Pinto, Goran Nenadic, Siew Kei Lam, Anil Anthony Bharath
https://arxiv.org/abs/2509.14594

SynBench: A Benchmark for Differentially Private Text Generation
Data-driven decision support in high-stakes domains like healthcare and finance faces significant barriers to data sharing due to regulatory, institutional, and privacy concerns. While recent generative AI models, such as large language models, have shown impressive performance in open-domain tasks, their adoption in sensitive environments remains limited by unpredictable behaviors and insufficient privacy-preserving datasets for benchmarking. Existing anonymization methods are often inadequate…