ARTIFICIAL INTELLIGENCE (AI) ACOUSTIC FEEDBACK SUPPRESSION

1 . An audio processing system, comprising:

an input adapted to receive an acoustic signal having a target audio component via a microphone;

an electroacoustic transducer;

an amplifier configured to amplify the acoustic signal and output an amplified signal having an amplified target audio component via the electroacoustic transducer; and

an artificial intelligence (AI) system having a machine learning model that processes the acoustic signal prior to amplification to produce a dynamic filter, wherein the AI system applies the dynamic filter to the acoustic signal to suppress feedback in the amplified signal caused by the amplified target audio component being picked up by the microphone.

2 . The audio processing system of claim 1 , wherein the machine learning model comprises a neural network.

3 . The audio processing system of claim 2 , wherein the neural network includes one of a temporal convolutional network (TCN) or a convolutional long short term memory (ConvLSTM) network.

4 . The audio processing system of claim 1 , wherein the AI system transforms the acoustic signal into a sequence of spectral frames that are inputted to the machine learning model.

5 . The audio processing system of claim 4 , wherein each spectral frame includes approximately 100-300 frequency bins.

6 . The audio processing system of claim 4 , wherein processing of the acoustic signal includes:

generating a spectral mask using the machine learning model for each spectral frame;

applying each spectral mask to an associated spectral frame to generate a sequence of filtered spectral frames; and

applying an inverse asymmetric-windowed fast Fourier transform (FFT) to the filtered spectral frames to generate a filtered time domain acoustic signal.

7 . The audio processing system of claim 1 , wherein the machine learning model directly generates a sequence of filtered spectral frames.

8 . The audio processing system of claim 1 , wherein the machine learning model directly generates a filtered time domain acoustic signal.

9 . The audio processing system of claim 1 , wherein the machine learning model is trained with training data that includes a database of target audio components and a database of feedback components, and wherein the machine learning model is trained to filter out the feedback components.

10 . The audio processing system of claim 9 , wherein the training data used to train the machine learning model further includes a noise component, and wherein the machine learning model is trained to filter out the noise component.

11 . The audio processing system of claim 1 , wherein the target audio component comprises at least one of speech or music.

12 . A public address (PA) system comprising the audio processing system of claim 1 .

13 . A hearing assist device comprising the audio processing system of claim 1 .

14 . A method comprising:

receiving an acoustic signal having a target audio component via a microphone input;

generating a dynamic filter from the acoustic signal using a machine learning model;

applying the dynamic filter to the acoustic signal to suppress feedback in the acoustic signal;

amplifying the dynamically filtered acoustic signal to generate an amplified signal having an amplified target audio component; and

outputting the amplified signal to an electroacoustic transducer;

wherein the feedback is caused by the amplified target audio component being picked up by the microphone input.

15 . The method of claim 14 , wherein the target audio component comprises at least one of speech or music.

16 . The method of claim 14 , wherein the machine learning model comprises a neural network and the dynamic filter comprises a spectral mask.

17 . The method of claim 16 , wherein the acoustic signal is transformed into a sequence of spectral frames that are inputted to the machine learning model.

18 . The method of claim 17 , wherein each spectral frame includes approximately 100-300 frequency bins.

19 . The method of claim 17 , wherein processing of the acoustic signal includes:

generating a unique spectral mask using the machine learning model for each spectral frame;

applying each unique spectral mask to an associated spectral frame to generate a sequence of filtered spectral frames; and

applying an inverse asymmetric-windowed fast Fourier transform (FFT) to the filtered spectral frames to generate a filtered time domain acoustic signal.

20 . The method of claim 14 , wherein the machine learning model is trained with training data that includes a database of target audio components and a database of feedback components, and wherein the machine learning model is trained to filter out the feedback components.