Deep learning driven multi-channel filtering for speech enhancement

A system for performing speech signal enhancement using a deep neural network, DNN, comprising:

• a processor;
• and
• memory having stored therein instructions that when executed by the processor
• extract a first plurality of features from i) a current frame of a multi-channel speech pickup and ii) first side information selected from the group consisting of a linear echo estimate, a diffuse signal component of the multi-channel speech pickup, or a noise estimate for the multi-channel speech pickup,
• produce, as an output of a first DNN, a first DNN-based speech presence probability value, first SPP value, for the current frame, wherein the first SPP value is produced by the first DNN in response to the extracted first plurality of features being input to the first DNN, and
• apply the first SPP value to configure a multi-channel filter whose input is the multi-channel speech pickup and whose output is a single audio signal.

A method for performing speech signal enhancement, comprising:

• performing acoustic echo cancellation upon a plurality of microphone signals, to produce i) a multi-channel speech pickup as a plurality of echo cancelled audio signals and ii) a plurality of linear echo estimates associated with the echo cancelled audio signals,
• extracting a plurality of features from i) a current frame of the multi-channel speech pickup and ii) side information selected from the plurality of linear echo estimates, a diffuse signal component, or a noise estimate of the multi-channel speech pickup;
• producing speech presence probability value, SPP value, for the current frame, wherein the SPP value is produced based on the extracted plurality of features;
• and
• applying the SPP value to configure a multi-channel filter whose input is the multi-channel speech pickup and whose output is a single audio signal.