2023/10/17 by Christian J. Steinmetz, Thomas Walther, Steinmetz, Christian J. +3 · 3 citations
Computer Science · Engineering · Neuroscience · #Acoustic Wave Phenomena Research #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Hearing Loss and Rehabilitation #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2310.11364
openalex publication_date 2023/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 .