eess.AS · 2026-08-10 · No. 80

Audio and Speech Processing, 2026-08-10.

3 new papers in eess.AS. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    LSEAD: A Privacy-Preserving LLM-Based Speech Analysis Framework for Early Alzheimer's Disease Screening

    Xin Wang, Yingchao Huang, Yuhan Su, Shanshan Yao, Wei Peng

    eess.AS · cs.AI · cs.LG

    Early diagnosis of Alzheimer's disease (AD) is critical for enabling timely interventions that may slow disease progression and improve patient outcomes. There is a growing need for AD detection methods that are non-invasive and cost-effective, especially in real-world clinical settings with diverse patient populations and recording conditions. Speech-based screening addresses these needs by using natural speech collected without specialized...

    arxiv.org/abs/2608.07378 · PDF

  2. 02

    Assessing AI-generated music detection in real-world broadcast monitoring

    David López-Ayala, Fernando García de la Cruz, Pablo Zinemanas, Emilio Molina, Martín Rocamora

    eess.AS · cs.AI

    The proliferation of AI-generated music in broadcast media raises concerns about transparency and fair compensation, but reliable detection under real broadcast conditions remains unresolved. Existing studies report substantial performance degradation in this domain, yet their evaluations are limited to synthetic broadcast data. To address this gap, we introduce BAMM (Broadcast AI-Music Monitoring), a 40-hour dataset of real-world television...

    arxiv.org/abs/2608.07359 · PDF

  3. 03

    How Much AI Is in This Track? Quantifying the Proportion of AI-Generated Stems in Hybrid Music Mixtures

    Fernando Garcia de la Cruz, David López-Ayala, Pablo Zinemanas, Emilio Molina, Martín Rocamora

    eess.AS · cs.AI · eess.SP

    AI-generated music is increasingly used at the stem level, with producers integrating synthetic drums, basslines, or vocals alongside human-performed instruments. However, current AI music detection systems are binary, treating tracks as either fully AI or fully human. In this paper, we reformulate AI music detection as a regression problem on a continuous AI energy ratio, alpha in [0, 1]. We propose a methodology that leverages a multi-track...

    arxiv.org/abs/2608.07285 · PDF

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