36 citations · 74 across the 20 of their papers we have counts for
5 papers · 1 filter
Steerable discovery of neural audio effects
Christian J. Steinmetz, Joshua D. Reiss
Applications of deep learning for audio effects often focus on modeling analog effects or learning to control effects to emulate a trained audio engineer. However, deep learning ap…
WaveBeat: End-to-end beat and downbeat tracking in the time domain
Christian J. Steinmetz, Joshua D. Reiss
Deep learning approaches for beat and downbeat tracking have brought advancements. However, these approaches continue to rely on hand-crafted, subsampled spectral features as input…
Direct design of biquad filter cascades with deep learning by sampling random polynomials
Joseph T. Colonel, Christian J. Steinmetz, Marcus Michelen +1
Designing infinite impulse response filters to match an arbitrary magnitude response requires specialized techniques. Methods like modified Yule-Walker are relatively efficient, bu…
Filtered Noise Shaping for Time Domain Room Impulse Response Estimation From Reverberant Speech
Christian J. Steinmetz, Vamsi Krishna Ithapu, Paul Calamia
Deep learning approaches have emerged that aim to transform an audio signal so that it sounds as if it was recorded in the same room as a reference recording, with applications bot…
Efficient neural networks for real-time modeling of analog dynamic range compression
Christian J. Steinmetz, Joshua D. Reiss
Deep learning approaches have demonstrated success in modeling analog audio effects. Nevertheless, challenges remain in modeling more complex effects that involve time-varying nonl…