Noise reduction using connectionist models

By S. Tamura, A. Waibel. 1988

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Joan Serrà
2. Szu-Wei Fu
3. Santiago Pascual
4. Dong Yu
5. Jinyu Li
6. Jordi Pons
7. Deliang Wang
8. Yu Tsao

Citations in the corpus

• A Wavenet for Speech Denoising (2017, methodology): “[13] used a 4-layered feed-forward network operating directly in the raw-audio domain to learn a noise-reduction mapping.”
• SEGAN: Speech Enhancement Generative Adversarial Network (2017, methodology): “In 1988, Tamura et al. [11] proposed a deep network that worked directly on the raw audio waveform, but they used feed-forward layers that worked frame-by-frame (60 samples) on a speaker-dependent and isolated-word database.”
• Raw waveform-based speech enhancement by fully convolutional networks (2017, methodology): “In [36, 37], Tamura and Waibel used an ANN to predict short window of clean speech waveforms from noisy ones.”
• Recurrent Neural Networks for Noise Reduction in Robust ASR (2012, unknown): “Neural network models have also been applied to learn a function to map a noisy utterance x to a clean version of the utterance y [3].”
• An investigation of deep neural networks for noise robust speech recognition (2013, background): “Index Terms— noise robustness, deep neural network, adaptive training, Aurora 4”