cs.SD · 2026-07-19 · No. 58

Sound, 2026-07-19.

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

01 — The papers

4 entries
  1. 01

    RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems

    David Ayllon, Alice Baird, Jeffrey Brooks, Franc Camps-Febrer, Jakub Piotr Cłapa, Theo Lebryk, Jens Madsen, Olya...

    cs.SD · cs.AI

    Current voice AI benchmarks typically evaluate isolated capabilities such as speech intelligibility, word error rate, or text-based dialogue quality, but they rarely test whether systems harness the acoustic information that distinguishes spoken language from its textual representation. To this end, we introduce the Real World Voice EQ Bench, a multidimensional benchmark for evaluating voice AI across text-to-speech (TTS), speech-to-speech...

    arxiv.org/abs/2607.14846 · PDF

  2. 02

    Large Audio Language Models for Spoofing-Aware Speaker Verification

    Sofya Savelyeva, Mariia Perunova, Evgeny Kushnir, Artem Dvirniak, Dmitrii Korzh, Oleg Y. Rogov

    cs.SD · cs.AI

    Recent advances in text-to-speech and voice cloning make high-quality spoofing inexpensive and scalable, threatening voice authentication systems, especially automatic speaker verification (ASV). Existing defenses mainly address this threat through binary countermeasures (CMs) for deepfake detection or spoofing-aware speaker verification (SASV), where current systems are dominated by modular ASV-CM fusion and cascaded pipelines. Although...

    arxiv.org/abs/2607.14753 · PDF

  3. 03

    MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music

    Scott H. Hawley

    cs.SD · cs.LG · eess.AS

    Rich internal representations of musical structure are essential for music understanding tasks such as machine-assisted music co-writing, yet self-supervised approaches for symbolic music representation remain underexplored, particularly those that encode the hierarchical multiscale nature of musical structures. We present MIDI-RAE-JEPA, combining a pitch- and time-shift equivariance objective with LeJEPA and a Swin Transformer V2 encoder to...

    arxiv.org/abs/2607.14537 · PDF

  4. 04

    Can Tokens Compete? Token Representations against Supervised CNN Backbones for BirdCLEF+ 2026

    Anthony Miyaguchi, Murilo Gustineli, Adrian Cheung

    cs.SD · cs.AI · cs.LG

    This paper details the DS@GT ARC team's approach to BirdCLEF+ 2026, multi-label detection of animal vocalizations in soundscapes from the Pantanal wetlands. The 2026 edition adds about an hour of labeled soundscapes, shifting the task toward supervised pipelines fit to the labeled set. First, we build a competitive supervised baseline that ensembles a frozen Perch v2 backbone, a trained HGNetV2-B0 sound-event-detection network, and a non-bird...

    arxiv.org/abs/2607.14474 · PDF

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