Corpus ID: 167217646

SignalTrain: Profiling Audio Compressors with Deep Neural Networks

@article{Hawley2019SignalTrainPA,
  title={SignalTrain: Profiling Audio Compressors with Deep Neural Networks},
  author={S. Hawley and Benjamin Colburn and S. I. Mimilakis},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.11928}
}
  • S. Hawley, Benjamin Colburn, S. I. Mimilakis
  • Published 2019
  • Computer Science, Engineering
  • ArXiv
  • In this work we present a data-driven approach for predicting the behavior of (i.e., profiling) a given non-linear audio signal processing effect (henceforth "audio effect. [...] Key Method Compressors were chosen because they are a widely used and important set of effects and because their parameterized nonlinear time-dependent nature makes them a challenging problem for a system aiming to profile "general" audio effects. Results from our experimental procedure show that the primary functional and auditory…Expand Abstract
    2 Citations
    InverSynth: Deep Estimation of Synthesizer Parameter Configurations From Audio Signals
    • 1
    • PDF
    Modeling Plate and Spring Reverberation Using A DSP-Informed Deep Neural Network

    References

    SHOWING 1-10 OF 29 REFERENCES
    Efficient Neural Audio Synthesis
    • 292
    • PDF
    Deep Learning for Tube Amplifier Emulation
    • 11
    • PDF
    Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
    • 233
    • PDF
    Time-Frequency Networks for Audio Super-Resolution
    • 18
    • PDF
    Adversarial Audio Synthesis
    • 146
    • PDF
    End-to-end music source separation: is it possible in the waveform domain?
    • 17
    • PDF
    Formant estimation and tracking: A deep learning approach.
    • 5
    • PDF