Audio detection and monitoring of respiration

An electronic device, comprising:

• a memory storing a multi-task recurrent network that includes a breath embedding layer and a noise embedding layer;
• and
• one or more processors configured to:
• obtain at least one audio recording of at least one breath cycle of a user from an audio device that is configured to output audio content provided by the electronic device;
• provide audio input data corresponding to the at least one audio recording to the multi-task recurrent network trained using a multi-task objective function to generate a respiratory rate, an indication of a breathing condition from a breath embedding layer of the multi-task recurrent network and an indication of a background noise of the at least one audio recording from a noise embedding layer of the multi-task recurrent network;
• obtain, as an output from the multi-task recurrent network, an indication of a background noise of the at least one audio recording from the noise embedding layer and at least one of the respiratory rate of the user or the indication of a breathing condition of the user;
• and
• provide the indication of the background noise and at least one of the respiratory rate or the breathing condition for display by a display of the electronic device.

A method of training a multi-task recurrent network for estimation of respiratory rates from audio data, the method comprising:

• providing input training data to the multi-task recurrent network, the input training data corresponding to a plurality of audio recordings;
• generating multiple training outputs with the multi-task recurrent network responsive to the input training data;
• and
• adjusting a plurality of parameters of a breath embedding layer of the multi-task recurrent network and a plurality of parameters of a noise embedding layer of the multi-task recurrent network using a multi-task objective function to compare the multiple training outputs of the multi-task recurrent network to output training data, the output training data including a known respiratory rate, a known breathing condition, and a known noise condition associated with the plurality of audio recordings.