Advancing the science of intelligent audio technology
Our research team publishes cutting-edge findings in top-tier journals and conferences, advancing the field of audio technology.
Deep learning approaches to audio enhancement and processing
Low-latency audio processing for live applications
3D audio positioning and binaural processing
Secure and private machine learning for audio
Explore our latest research contributions to the scientific community
This paper presents a novel approach to real-time audio enhancement using deep neural networks optimized for low-latency processing. Our method achieves a 40% improvement in audio quality while maintaining sub-5ms latency.
We introduce a reinforcement learning approach for adaptive noise cancellation that learns from user preferences and environmental conditions to provide personalized audio experiences.
This work explores quantum-inspired classical algorithms for audio signal processing, demonstrating exponential speedups for certain audio analysis tasks while remaining implementable on classical hardware.
We present a deep learning model that predicts perceptual audio quality with human-level accuracy, enabling automated quality assessment for audio processing systems.
This paper introduces a federated learning framework for personalized audio processing that preserves user privacy while enabling collaborative model improvement across distributed devices.
We propose a GAN-based approach for synthesizing realistic binaural audio from monophonic sources, enabling immersive 3D audio experiences without specialized recording equipment.
Interested in collaborating on audio technology research? We welcome partnerships with academic institutions and research organizations.