arXiv Machine Learning

Synth-JEPA: Joint Embedding Prediction for Renderer-Free Synthesizer Parameter Search

Synth-JEPA introduces a renderer‑free approach to synthesizer parameter search by learning mutually predictive audio and parameter representations from paired synthesizer data. During inference, candidate parameters are scored directly in this learned space, avoiding the need to render each candidate and shaping audio geometry through parameter correspondences. Evaluations on Surge XT and out‑of‑domain datasets show Synth‑JEPA outperforms inverse models, direct search, and learned proxy objectives, with listeners preferring its matches in 85% of pairwise tests.

arXiv AI

Sep 24

SsgCaps: A controlled dataset for the evaluation of sound scene generation algorithms

SsgCaps is a publicly available dataset of human-engineered sound scenes, each paired with a precisely structured prompt that guides the sampling process. The prompts are drawn from a predefined action-based typology, enabling extensive yet plausible sampling. A comparative quantitative analysis shows only small differences between the open and private versions, supporting the recommendation of the open version for benchmarking sound scene generation algorithms.

By Modan Tailleur (LS2N), Junwon Lee (LS2N), Laurie M Heller (LS2N), Mathieu Lagrange (LS2N), Keunwoo Choi, Brian McFee, Keisuke Imoto, Yuki Okamoto

benchmarks

arXiv AI

Sep 17

CPR: Combining global composing, local performing and full-sequence refining in piano rendering with continuous autoregressive modelling

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

diffusion efficiency safety

arXiv Machine Learning

Jun 16

From Physics to Representation: Audio Learning with Synthetic Pre-training via Procedural Generation

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

llms diffusion benchmarks safety

arXiv AI

Jun 26

Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training

arXiv:2512. 02652v2 Announce Type: replace-cross Abstract: Existing methods for expressive music performance rendering, a conditional generation task that aims to generate a human-like performance from a symbolic score, rely on supervised learning over small labeled datasets, which limits scaling of both data volume and model size, despite the availability of vast unlabeled music, as in vision and language.

By Hong-Jie You, Jie-Jing Shao, Xiao-Wen Yang, Lin-Han Jia, Lan-Zhe Guo, Yu-Feng Li

llms efficiency benchmarks

arXiv AI

Jul 1

BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations

arXiv:2606. 30700v1 Announce Type: cross Abstract: Self-supervised learning enables audio representations that transfer across domains and tasks.

By Ludovic K. Tuncay (IRIT-SAMoVA), Etienne Labb\'e (IRIT-SAMoVA), Thomas Pellegrini (IRIT-SAMoVA)

llms benchmarks

arXiv Machine Learning

Jul 28

Music-Source-Separation-Training (MSST): A Unified Framework for Training and Evaluating Music Demixing Models

arXiv:2607. 23395v1 Announce Type: cross Abstract: Music Source Separation (MSS), the task of recovering individual sound components (stems) from a polyphonic mixture, is central to applications ranging from karaoke and remixing to audio restoration and content production.

By Roman Solovyev, Ilya Kiselev, Alexander Stempkovskiy, Tatiana Gabruseva

fine-tuning benchmarks

arXiv AI

4d ago

Video-to-Music Generation for Gameplay Videos

arXiv:2609.31810v2 Announce Type: replace-cross Abstract: Video-to-music models have advanced considerably in the last few years, particularly in film and music video applications. In this paper, we...

By Felipe Marra, Lucas N. Ferreira

llms fine-tuning benchmarks

arXiv AI

Jun 10

Dual-Branch Gated Fusion for Open-Set Audio Deepfake Source Tracing

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

benchmarks

arXiv Machine Learning

Jul 27

Reflector: Arrangement-Aware Harmonic Retrieval for Sample-Based Composition

arXiv:2607. 22413v1 Announce Type: cross Abstract: Sample retrieval tools can help composers find harmonically compatible material, but querying from a fixed reference sample becomes less informative as arrangements evolve and the harmonic context shifts with each musical decision.

By Austin Rockman

rag

arXiv Machine Learning

Jul 31

Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

arXiv:2607. 27909v1 Announce Type: cross Abstract: Objective evaluation of expressive MIDI piano performances typically relies on attribute statistics such as timing, velocity, and duration of individual notes.

By Dmitrii Gavrilev, Ilya Borovik, Vladimir Viro

rag safety

arXiv Machine Learning

Jul 1

Step-by-Step Video-to-Audio Synthesis via Negative Audio Guidance

arXiv:2506. 20995v4 Announce Type: replace-cross Abstract: We propose a step-by-step video-to-audio (V2A) generation method that provides finer control over the generation process and more realistic audio synthesis.

By Akio Hayakawa, Masato Ishii, Takashi Shibuya, Yuki Mitsufuji

fine-tuning

arXiv Machine Learning

Jun 11

PianoKontext: Expressive Performance Rendering from Deadpan Context

arXiv:2606. 12282v1 Announce Type: cross Abstract: Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes.

By Dmitrii Gavrilev

rag diffusion

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