36 citations · 74 across the 20 of their papers we have counts for
5 papers · 1 filter
ATGNN: Audio Tagging Graph Neural Network
Shubhr Singh, Christian J. Steinmetz, Emmanouil Benetos +2
Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging. Recent works have shown that despite stacking multiple layers,…
High-Fidelity Noise Reduction with Differentiable Signal Processing
Christian J. Steinmetz, Thomas Walther, Joshua D. Reiss
Noise reduction techniques based on deep learning have demonstrated impressive performance in enhancing the overall quality of recorded speech. While these approaches are highly pe…
General Purpose Audio Effect Removal
Matthew Rice, Christian J. Steinmetz, George Fazekas +1
Although the design and application of audio effects is well understood, the inverse problem of removing these effects is significantly more challenging and far less studied. Recen…
Modulation Extraction for LFO-driven Audio Effects
Christopher Mitcheltree, Christian J. Steinmetz, Marco Comunità +1
Low frequency oscillator (LFO) driven audio effects such as phaser, flanger, and chorus, modify an input signal using time-varying filters and delays, resulting in characteristic s…
Leveraging Neural Representations for Audio Manipulation
Scott H. Hawley, Christian J. Steinmetz
We investigate applying audio manipulations using pretrained neural network-based autoencoders as an alternative to traditional signal processing methods, since the former may prov…