Supervised Speech Separation Using Deep Neural Networks
Yuxuan Wang · OhioLink ETD Center (Ohio Library and Information Network) · 2015
Speech is crucial for human communication.However, speech communication for both humans and automatic devices can be negatively impacted by background noise, which is common in real environments.Due to numerous applications, such as hearing prostheses and automatic speech recognition, separation of target speech from sound mixtures is of great importance.Among many techniques, speech separation using a single microphone is most desirable from an application standpoint.The resulting monaural speech separation problem has been a central problem in speech processing for several decades.However, its success has been limited thus far.Time-frequency (T-F) masking is a proven way to suppress background noise.With T-F masking as the computational goal, speech separation reduces to a mask