The paper investigates how much original audio information remains in frozen representations from various pretrained audio encoders by attempting to reconstruct five‑second, 44.1‑kHz stereo music clips. Using a shared Stable Audio Open latent diffusion decoder, the authors reconstruct audio from embeddings produced by supervised classifiers (VGGish, ConvNeXt), an audio‑text contrastive model (CLAP), and a waveform‑reconstruction model (EnCodec). Results on the Million Song Dataset reveal that reconstructability varies across encoder families and that embeddings exposing finer temporal or spectral detail enable better recovery of source content, even for compressed, task‑oriented embeddings.
By Marios Glytsos, Brian McFee
arXiv:2607. 08545v1 Announce Type: cross Abstract: End-to-end neural audio models achieve high-fidelity compression and generation.
By Nicole Cosme-Clifford
The paper introduces CPR, a piano rendering framework that combines continuous autoregressive modeling with local flow matching and full‑sequence refinement. It predicts continuous hidden states, generates 24 kHz acoustic latents, and upsamples to 48 kHz, while new techniques BREPA and MT‑RoPE enhance musical semantics and cross‑modal alignment.
By Chong Jing, Junan Zhang, Zhizheng Wu
arXiv:2606. 14791v1 Announce Type: cross Abstract: Self-supervised learning advances audio representation for multimedia analysis.
By Fengrui Liu, Ruiyang Huang, Qijian Zheng, Yuanfang Wang, Feng Liu
arXiv:2608. 14916v1 Announce Type: cross Abstract: AI-generated music detectors are commonly evaluated against original songs, but real-world uploads are often remixed, re-encoded, pitch-shifted, or otherwise edited.
By Alexandru-Stefan Morosanu, Valerian Cecan, Stefan-Daniel Achirei, Laura Erhan
arXiv:2608. 15690v1 Announce Type: cross Abstract: Text-to-audio-video (T2AV) generation models produce a video and its soundtrack from a textual description, but offer no control over whose voice speaks in the output.
By Ivan Mikheev, Viacheslav Vasilev, Anna Dmitrienko, Alexey Letunovskiy, Ivan Kirillov, Kirill Chernyshev, Denis Dimitrov
arXiv:2605.23954v2 Announce Type: replace-cross
Abstract: Large Audio Language Models (LALMs) remain vulnerable to acoustic noise, which can obscure task-relevant evidence and produce unreliable resp...
By Kaiwen Luo, Chunxi Luo, Liang Lin, Yuxuan Li, Zhenhong Zhou, Junhao Dong, Yingjie Zhou, Zhendong Chu
arXiv:2606. 03803v1 Announce Type: cross Abstract: We present LiveBand, a real-time system that generates high-fidelity music accompaniments to live audio input, respecting strict causal constraints.
By Marco Pasini, Javier Nistal, Mathias Rose Bjare, Stefan Lattner, George Fazekas
SAGE, a 105‑million‑parameter semantic audio generative encoder, compresses music waveforms into compact latent representations by distilling embeddings from a pretrained audio‑text model. It achieves a three‑way balance—high reconstruction quality, rich semantic latent structure, and fast inference—running three times faster than Stable Audio Open while matching or surpassing larger models in listening‑test quality and objective metrics. Across all nineteen latent‑semantics probing tasks, SAGE sets new state‑of‑the‑art performance both within and outside its training domain.
By Francesco Brigante, Luca Cerovaz, Davide Marincione, Giorgio Strano, Luca Zhou, Emanuele Rodol\`a, Michele Mancusi
Spot, Separate, and Enhance (SSE) is a multimodal, user‑guided generative model for audio remixing and enhancement. It rebalances audio, removes unwanted sources, and reduces reverberation using video and textual guidance. The authors introduce the DegradedMix dataset and adopt generative evaluation metrics, showing SSE outperforms existing baselines in controllability and remixing quality.
By Ilpo Viertola, Giulio Cengarle, Gouthaman KV, Daniel Arteaga, Lie Lu
arXiv:2606. 10223v1 Announce Type: cross Abstract: Attributing a synthetic utterance to its originating system remains an open challenge: closed-set models fail to reject unseen synthesizers and produce overconfident predictions.
By Awais Khan, Kutub Uddin, Khalid Malik
X-AVDD is a cross‑attention‑based audio‑visual dataset distillation method that explicitly models inter‑modal relationships during synthesis. By decoupling cross‑modal alignment from joint optimization, it reduces memory usage and end‑to‑end GPU‑hour cost by about eight times while producing highly aligned synthetic data. Empirical results show significant performance gains over prior methods, improving top‑1 test accuracy on VGGSound from 8.2 % to 24.0 % at 10 samples‑per‑class and enabling efficient scaling to complex architectures such as AudioCLIP.
By Saumyaranjan Mohanty, Aravind Reddy, Konda Reddy Mopuri