  Noise reduction techniques based on deep learning have demonstrated
impressive performance in enhancing the overall quality of recorded speech.
While these approaches are highly performant, their application in audio
engineering can be limited due to a number of factors. These include operation
only on speech without support for music, lack of real-time capability, lack of
interpretable control parameters, operation at lower sample rates, and a
tendency to introduce artifacts. On the other hand, signal processing-based
noise reduction algorithms offer fine-grained control and operation on a broad
range of content, however, they often require manual operation to achieve the
best results. To address the limitations of both approaches, in this work we
introduce a method that leverages a signal processing-based denoiser that when
combined with a neural network controller, enables fully automatic and
high-fidelity noise reduction on both speech and music signals. We evaluate our
proposed method with objective metrics and a perceptual listening test. Our
evaluation reveals that speech enhancement models can be extended to music,
however training the model to remove only stationary noise is critical.
Furthermore, our proposed approach achieves performance on par with the deep
learning models, while being significantly more efficient and introducing fewer
artifacts in some cases. Listening examples are available online at
https://tape.it/research/denoiser .

Sound;Audio and Speech Processing