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

Mixed signal

Clean signal

Demixed signal

• STOI measure on other noises

Seen speakers	Unseen speakers

• STOI measure on bus noises

Seen speakers	Unseen speakers

• STOI measure on caf noises

Seen speakers	Unseen speakers