arXiv AI June 2, 2026 By Maris Basha, Anja Zai, Sabine Stoll, Richard Hahnloser

VocSim: A Training-free Benchmark for Zero-shot Content Identity in Single-source Audio

arXiv:2512. 10120v2 Announce Type: replace-cross Abstract: General-purpose audio representations aim to map acoustically variable instances of the same event to nearby points, resolving content identity in a zero-shot setting.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

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Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings

arXiv:2607. 03806v1 Announce Type: cross Abstract: Audio foundation models are widely adopted as general-purpose feature extractors, yet the internal structure of their learned representations remains insufficiently understood.

By H\'ector Martel, Joe Hennessy-Priest, Taemin Cho

rag multimodal

arXiv Machine Learning

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A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification

arXiv:2608. 14824v1 Announce Type: cross Abstract: We present a parameter-free episodic evaluation of nearest-centroid classification for elephant vocalisations on fixed pretrained acoustic embeddings, across the Elephant Voices (EV) and Linguistic Data Consortium (LDC) datasets.

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Hugging Face Trending Papers

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Confucius4-TTS: Transcript-Free Cross-Lingual Zero-Shot TTS with a Learnable Speaker Encoder

Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time.

llms multimodal benchmarks

arXiv Machine Learning

Sep 15

MAST: Label-Efficient, Robust, and Generalizable Sound Detection for Biodiversity Monitoring via Masked Audio Pretraining and Self-Training

arXiv:2609.15221v1 Announce Type: cross Abstract: Passive acoustic monitoring can measure biodiversity at larger scales, but time--frequency annotation of animal vocalizations is expensive, site-spec...

By Tianyi Xu, Daniel Pimentel-Alarc\'on, Zuzana Bu\v{r}ivalov\'a, Claudia Sol\'is-Lemus

arXiv AI

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Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors

arXiv:2606. 19325v1 Announce Type: cross Abstract: Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings.

By Michael Finkelson, Daniel Segal, Eitan Richardson, Shahar Armon, Nani Goldring, Poriya Panet, Nir Zabari, Benjamin Brazowski, Or Patashnik, Yoav HaCohen

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Hugging Face Trending Papers

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Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors

Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings. These systems operate within speech-only pipelines that produce clean vocal sequences without the ambient texture of real conversations.

rag benchmarks

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Related stories

Probing Low-Level Acoustic Attribute Encoding in CLAP Audio Embeddings

A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification

Confucius4-TTS: Transcript-Free Cross-Lingual Zero-Shot TTS with a Learnable Speaker Encoder

MAST: Label-Efficient, Robust, and Generalizable Sound Detection for Biodiversity Monitoring via Masked Audio Pretraining and Self-Training

Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors

Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors