mlsanityData to 7 Oct 2026

Data augmentation and loss normalization for deep noise suppression

arXiv 2008.06412 ↗First posted 14 Aug 2020eess.AScited by 23

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Abstract (arXiv)

Speech enhancement using neural networks is recently receiving large attention in research and being integrated in commercial devices and applications. In this work, we investigate data augmentation techniques for supervised deep learning-based speech enhancement. We show that not only augmenting SNR values to a broader range and a continuous distribution helps to regularize training, but also augmenting the spectral and dynamic level diversity. However, to not degrade training by level augmentation, we propose a modification to signal-based loss functions by applying sequence level normalization. We show in experiments that this normalization overcomes the degradation caused by training on sequences with imbalanced signal levels, when using a level-dependent loss function.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 5; 2024: 3; 2025: 8; 2026: 4.

23 papers.

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  1. 2021SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processingcited by 177
  2. 2024MaskSR: Masked Language Model for Full-band Speech Restorationcited by 24
  3. 2025AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancementcited by 23
  4. 2021Real-time Denoising and Dereverberation with Tiny Recurrent U-Netcited by 16
  5. 2023A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentationcited by 13
  6. 2023Ultra Low Complexity Deep Learning Based Noise Suppressioncited by 7
  7. 2022Complex-Valued Time-Frequency Self-Attention for Speech Dereverberationcited by 6
  8. 2023Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exitingcited by 6
  9. 2024A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and Recognitioncited by 5
  10. 2025Adaptive Slimming for Scalable and Efficient Speech Enhancementcited by 4
  11. 2024Hold Me Tight: Stable Encoder-Decoder Design for Speech Enhancementcited by 2
  12. 2023CheapNET: Improving Light-weight speech enhancement network by projected loss functioncited by 1
11 more
  1. 2023Improving the Intent Classification accuracy in Noisy Environmentcited by 0
  2. 2025How much to Dereverberate? Low-Latency Single-Channel Speech Enhancement in Distant Microphone Scenarioscited by 0
  3. 2025ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integrationcited by 0
  4. 2025MoE-Gyro: Self-Supervised Over-Range Reconstruction and Denoising for MEMS Gyroscopescited by 0
  5. 2025Advances in Intelligent Hearing Aids: Deep Learning Approaches to Selective Noise Cancellationcited by 0
  6. 2025A Small-footprint Acoustic Echo Cancellation Solution for Mobile Full-Duplex Speech Interactionscited by 0
  7. 2025Alternating Approach-Putt Models for Multi-Stage Speech Enhancementcited by 0
  8. 2026Towards Lightweight Adaptation of Speech Enhancement Models in Real-World Environmentscited by 0
  9. 2026Real-Time Band-Grouped Vocal Denoising Using Sigmoid-Driven Ideal Ratio Maskingcited by 0
  10. 2026QC-GAN: A Parameter-Efficient Quaternion Conformer GAN for High-Fidelity Speech Enhancementcited by 0
  11. 2026Does per-frame early exit pay? A compute-matched study of dynamic depth for on-device speech enhancementcited by 0