Generating sensor signals in isotropic noise fields.

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WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing (2021, methodology): “In addition, we add simulated isotropic noise [75] with a 0–10 dB signal to noise ratio.”
Continuous Speech Separation: Dataset and Analysis (2020, background): “Following [40], multi-channel isotropic noise signals are generated and added”
Continuous Speech Separation with Conformer (2020, methodology): “In addition, we add simulated isotropic noise [31] with a 0–10 dB signal to noise ratio.”
Don’t Shoot Butterfly with Rifles: Multi-Channel Continuous Speech Separation with Early Exit Transformer (2020, background): “Simulated isotropic noise [26] is also added at a 0–10 dB signal to noise ratio.”
Ultra Fast Speech Separation Model with Teacher Student Learning (2021, background): “Simulated isotropic noise [24] is also added at a 0–10 dB signal to noise ratio.”
Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained With Noise Signals (2018, methodology): “A 40 s long sample of multichannel diffuse babble noise was generated using the acoustic noise field generator [36], assuming an isotropic spherically diffuse noise field.”