Learnable heuristics to optimize a multi-hypothesis filtering system
View Patent ↗Some disclosed methods involve receiving microphone signals from a microphone system, including signals corresponding to one or more sounds detected by the microphone system. Some methods may involve determining, via a trained neural network, a filtering scheme for the microphone signals, the filtering scheme including one or more filtering processes. The trained neural network may be configured to implement one or more subband-domain adaptive filter management modules. Some methods may involve applying the filtering scheme to the microphone signals, to produce enhanced microphone signals.
1 . An audio device, comprising:
a microphone system; and
a control system configured to:
receive microphone signals from the microphone system, the microphone signals including signals corresponding to one or more sounds detected by the microphone system;
determine, via a trained neural network, a filtering scheme for the microphone signals, the filtering scheme including one or more filtering processes, wherein the trained neural network is configured to implement one or more subband-domain adaptive filter management modules, the one or more subband-domain adaptive filter management modules configured to control one or more multichannel, multi-hypothesis adaptive filter blocks;
apply the filtering scheme to the microphone signals, to produce enhanced microphone signals.
2 . The audio device of claim 1 , wherein the control system is further configured to implement a subband-domain acoustic echo canceller (AEC) and wherein the filtering scheme includes an echo cancellation process.
3 . The audio device of claim 2 , further comprising a loudspeaker system, wherein the control system is further configured to implement a renderer for producing rendered local audio signals and for providing the rendered local audio signals to the loudspeaker system and to the subband-domain AEC.
4 . The audio device of claim 2 , wherein the control system is configured for providing reference non-local audio signals to the subband-domain AEC, the reference non-local audio signals corresponding to audio signals being played back by one or more other audio devices.
5 . The audio device of claim 1 , wherein the control system is further configured to implement a noise compensation module and wherein the filtering scheme includes a noise compensation process.
6 . The audio device of claim 1 , wherein the control system is further configured to implement a dereverberation module and wherein the filtering scheme includes a dereverberation process.
7 . The audio device of claim 1 , wherein the control system is further configured to implement a beam steering module and wherein the filtering scheme includes a beam steering process.
8 . The audio device of claim 1 , wherein the control system is further configured to:
implement an automatic speech recognition module; and
provide the enhanced microphone signals to the automatic speech recognition module.
9 . The audio device of claim 1 , wherein the control system is further configured to:
implement a telecommunications module; and
provide the enhanced microphone signals to the telecommunications module.
10 . The audio device of claim 1 , wherein the trained neural network comprises a recurrent neural network.
11 . The audio device of claim 10 , wherein the recurrent neural network comprises a gated adaptive filter unit.
12 . The audio device of claim 11 , wherein the gated adaptive filter unit includes a reset gate, an update gate and a keep gate.
13 . The audio device of claim 11 , wherein the gated adaptive filter unit includes an adaptation gate.
14 . The audio device of claim 11 , further comprising a square law module configured to generate a plurality of residual power signals based, at least in part, on the microphone signals.
15 . The audio device of claim 14 , wherein the square law module is configured to generate the plurality of residual power signals based, at least in part, on reference signals corresponding to audio being played back by the audio device and one or more other audio devices.
16 . The audio device of claim 15 , further comprising a selection block configured to select the enhanced microphone signals based, at least in part, on a minimum residual power signal of the plurality of residual power signals.
17 . The audio device of claim 1 , wherein the control system is further configured to implement post-deployment training of the trained neural network, the post-deployment training occurring after the audio device has been deployed and activated in an audio environment.