Physics-Informed Neural Engine Sound Modeling with Differentiable Pulse-Train Synthesis
📰 ArXiv cs.AI
Learn to model engine sounds using physics-informed neural networks and differentiable pulse-train synthesis, enabling more realistic audio generation
Action Steps
- Implement the Pulse-Train-Resonator (PTR) model using a deep learning framework like PyTorch or TensorFlow
- Train the PTR model on a dataset of engine sounds to learn the underlying pulse shapes and temporal structure
- Use the trained model to generate synthetic engine audio by sampling from the learned pulse train distribution
- Evaluate the generated audio using metrics such as spectral similarity and perceptual quality
- Refine the model by incorporating additional physical constraints or modifying the pulse train synthesis architecture
Who Needs to Know This
Audio engineers and researchers working on sound synthesis can benefit from this technique to create more realistic engine sounds, while machine learning engineers can apply this method to other domains requiring physics-informed modeling
Key Insight
💡 Physics-informed neural networks can be used to model complex physical systems like engine sounds, enabling more realistic and controllable audio generation
Share This
🚀 Generate realistic engine sounds with physics-informed neural networks and differentiable pulse-train synthesis! 🎧 #audioynthesis #physicssimulated
Key Takeaways
Learn to model engine sounds using physics-informed neural networks and differentiable pulse-train synthesis, enabling more realistic audio generation
Full Article
Title: Physics-Informed Neural Engine Sound Modeling with Differentiable Pulse-Train Synthesis
Abstract:
arXiv:2603.09391v2 Announce Type: replace-cross Abstract: Engine sounds originate from sequential exhaust pressure pulses rather than sustained harmonic oscillations. While neural synthesis methods typically aim to approximate the resulting spectral characteristics, we propose directly modeling the underlying pulse shapes and temporal structure. We present the Pulse-Train-Resonator (PTR) model, a differentiable synthesis architecture that generates engine audio as parameterized pulse trains alig
Abstract:
arXiv:2603.09391v2 Announce Type: replace-cross Abstract: Engine sounds originate from sequential exhaust pressure pulses rather than sustained harmonic oscillations. While neural synthesis methods typically aim to approximate the resulting spectral characteristics, we propose directly modeling the underlying pulse shapes and temporal structure. We present the Pulse-Train-Resonator (PTR) model, a differentiable synthesis architecture that generates engine audio as parameterized pulse trains alig
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