Papers for

game audio developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Procedural audio pre training improves with source scaling and mask tuning

Rethinking Procedural Audio Pre-training: Source Scaling and Objective Adaptation

Abstract: Procedural audio has emerged as a viable source for transferable audio representation learning, but its design principles remain unclear.We revisit two questions: how a procedural source should be scaled, and whether training choices developed on natural audio should transfer unchanged to procedural data.Using a controlled source, we separate scale into formula-class coverage C and within-class rendering diversity I.Experiments with FDSL and AudioMAE show that these two forms of scale provide different benefits and depend on the learning formulation and downstream task. A matched AudioMAE study further shows that procedural audio favors low mask ratios (10%--25%), whereas AudioSet-28K favors 50%--75%. Shared-codebook analysis reveals lower patch diversity and stronger temporal predictability in procedural audio. These results motivate source-aware procedural pre-training, where source scaling and learning configuration are considered jointly.Code is available at https://github.com/Cross-Innovation-Lab/Formula-Bank.

Mon 14 SeptSoundArtificial Intelligence

The gist

Procedural audio, which is artificially generated sound, can help teach machines to understand audio better. The authors studied how to best create and use these sounds to teach machines by looking at two things: how varied the sound formulas are and how much variety there is in the sounds they produce. They found that different ways of making these sounds help in different learning tasks, and that settings used for regular audio don't always work well with procedural audio. Their work suggests adjusting training methods depending on the kind of audio source being used.

Open → 2609.15067v1

Neural networks synthesize controllable musical distortion tones in real time

Learned Continuous Synthesis of Quadratic Difference Tone Spectra

Abstract: Quadratic difference tones (QDTs) are a species of auditory distortion product in which a "phantom" pure tone, absent from the acoustic signal, is clearly audible to listeners. Exploiting this phenomenon, one can synthesize harmonically rich tones for musical purposes, a technique called Quadratic Difference Tone Spectrum (QDTS) synthesis. Previous works have introduced numerical methods to synthesize QDTS based on the distortion function, which links a target QDTS and an overtone-structured carrier signal. While accurate, these methods were stochastic and discontinuous, making them difficult to control for musical purposes and effectively limiting them to stationary signals. This paper proposes a neural network-based approach that learns an approximate inverse of the distortion mapping in an autoencoder-like configuration, producing a continuous approximation that addresses prior limitations. Experimental results show that, although slightly less numerically precise, the method is sufficient for perceptual and musical applications. We also implement a real-time version in Max and evaluate its performance. Various sound examples demonstrate its expressive and musical potential. The source code, audio examples, tutorials, and software accompanying this work are available at https://cordutie.github.io/projects/qdts.html

Wed 9 SeptSound

The gist

Sometimes, people hear extra tones called quadratic difference tones (QDTs) that aren't actually played but are created by the ear. These tones can make music sound richer and more interesting. The authors created a neural network that learns how to produce these tones more smoothly and continuously, overcoming problems in older methods that were harder to control and only worked on steady sounds. While this new method is slightly less precise in calculations, it sounds good enough for making music and runs in real time. They also made easy-to-use software so musicians can try it out.

Open → 2609.10913v1