In this project, we implement neural Turing machine (NTM) for sequential signals of speech and noise in presence of different speakers and noise types. NTM is a memory-augmented neural network which is equipped with external memory to learn long sequential data. The information is stored with attention mechanism and read-writing scheme. For more details about NTM, you can refer to Neural Turing Machine. The system architecture and experimental settings are shown in Memory Augmented Neural Network for Source Separation.
- Hardware:
- CPU: Intel Core i7-4930K @3.40 GHz
- RAM: 64 GB DDR3-1600
- GPU: NVIDIA Tesla K20c 6 GB RAM
- Tensorflow 0.12
- Dataset
- Wall Street Journal Corpus
- Noises are collected from freeSFX and AudioMicro
- An example of demixed signal
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| Mixed signal |
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| Clean signal |
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| Demixed signal |
- STOI measure on other noises
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| Seen speakers | Unseen speakers |
- STOI measure on bus noises
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| Seen speakers | Unseen speakers |
- STOI measure on caf noises
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| Seen speakers | Unseen speakers |