cs.SD · 2026-09-01 · No. 102

Sound, 2026-09-01.

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

01 — The papers

3 entries
  1. 01

    CoJEPA: Combining Contrastive Learning and JEPA for Global-Local Music Representations

    Gabriel Meseguer-Brocal, Yuexuan Kong, Romain Hennequin

    cs.SD · cs.AI · cs.LG · eess.AS · eess.SP

    Joint-Embedding Predictive Architecture (JEPA) has shown strong performance in learning rich representations through self-supervised prediction in latent space. However, it typically relies on teacher--student architecture with an EMA to stabilise training, and can tend to yield uninformative representations. Contrastive learning is stable to train and produces strong global representations, but remains limited on local tasks by the global...

    arxiv.org/abs/2608.30974 · PDF

  2. 02

    MusGU+: Toward a Musician-Centered Evaluation Framework and Discovery Tool for Generative Music AI

    Laura Ibáñez-Martínez, Roser Batlle-Roca, Xavier Serra, Martín Rocamora

    cs.SD · cs.AI · cs.CY · eess.AS

    Generative music systems are increasingly presented as tools that democratize music creation, yet their practical suitability for musicians remains underexplored. Prior work includes openness-focused evaluation frameworks, such as MusGO (Music-Generative Open AI), as well as qualitative studies of musicians' experiences with generative systems. However, these approaches do not support systematic comparison or early-stage discovery of models...

    arxiv.org/abs/2608.30940 · PDF

  3. 03

    Stride-k Subsampling: Train-Free Audio Token Reduction for Whisper

    Chanhee Cho, Junhyuk Choi, Bugeun Kim

    cs.SD · cs.AI

    Whisper exposes speech through a fixed 1500-token encoder interface, now a default representation for ASR decoders and Whisper-based speech language models (SpeechLMs), yet its redundancy remains largely unexamined. We propose stride-k subsampling, a deterministic indexing operation that retains every k-th token after the convolutional stem or encoder transformer. Across five Whisper scales, k=2 preserves baseline WER at both positions, with...

    arxiv.org/abs/2608.30927 · PDF

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