arXiv:2608. 13724v1 Announce Type: cross Abstract: PLAUD (Performative Latents and Unsupervised DDSP) is a neural synthesizer and Max for Live instrument for live electronic music, built on NoiseBandNet and trained on small personal sound corpora.
arXiv:2606. 24307v1 Announce Type: cross Abstract: Interactive music and live performance relies on real-time human expression, but modern generative music AI remains largely absent from this domain due to its prohibitive inference latency and offline rendering paradigm.
arXiv:2609. 04289v1 Announce Type: new Abstract: LETHE (Latent-parameter Evolution with Temporal Hierarchical quasi-Equilibrium) is a self-referential sonic-oblivion system implemented in SuperCollider.
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.
arXiv:2608. 03742v1 Announce Type: cross Abstract: Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of variation and contextual adaptability.
arXiv:2608. 06638v1 Announce Type: cross Abstract: Mechanistic interpretability of music generation has concentrated on audio models, leaving symbolic models largely unexplored.
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.
The paper introduces a lightweight technique to steer the pitch content of audio generated by the Stable Audio Open diffusion model. A small convolutional probe (~125k parameters) is trained to decode frame‑level pitch‑class activations from the model’s latent space using paired audio and MIDI data. During inference, the frozen probe acts as a differentiable loss, guiding generation toward a user‑specified pitch‑class sequence without retraining the base model, and improves melodic coherence by 2.4× over the unguided baseline.
arXiv:2601. 04867v3 Announce Type: replace-cross Abstract: Modulation effects such as phasers, flangers and chorus effects are heavily used in conjunction with the electric guitar.
arXiv:2603. 07584v2 Announce Type: replace-cross Abstract: Computational engine sound modeling is central to the automotive audio industry, particularly for active sound design applications and virtual prototyping.
arXiv:2607. 08863v1 Announce Type: cross Abstract: We present Clean2FX, a study and demo of label-conditioned clean-to-effect transformation for electric guitar audio.
SpanSynth-Edit is a flow‑matching model that enables MIDI‑guided synthesis and editing of multi‑instrument audio mixtures using low‑frame‑rate scalar‑quantised latents. It encodes instrument‑labelled note lifecycles as unordered event sets, pools them into a conditioning vector per audio‑latent frame, and uses contextual audio for instrument‑specific timbre guidance. The model supports editing by resynthesising target regions from revised MIDI and demonstrates competitive performance on single‑ and multi‑instrument benchmarks, including within‑frame onset control.
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.