Abstract: Examples described herein involve detecting known environmental conditions of using a neural network. An example implementation involves a playback device receiving data indicating a response of a listening environment to audio output of one or more playback devices as captured by a microphone and determining an input vector for a neural network. The playback device provides the determined input vector to the neural network, which includes an output layer comprising neurons that correspond to respective environmental conditions. The playback device detects that the input vector caused one or more neurons of the neural network to fire such that the neural network indicates that one or more particular environmental conditions are present in the listening environment. The playback device adjusts audio output of the one or more playback devices to at least partially offset the one or more particular environmental conditions.

Abstract: Examples described herein involve detecting known environmental conditions of using a neural network. An example implementation involves a playback device receiving data indicating a response of a listening environment to audio output of one or more playback devices as captured by a microphone and determining an input vector for a neural network. The playback device provides the determined input vector to the neural network, which includes an output layer comprising neurons that correspond to respective environmental conditions. The playback device detects that the input vector caused one or more neurons of the neural network to fire such that the neural network indicates that one or more particular environmental conditions are present in the listening environment. The playback device adjusts audio output of the one or more playback devices to at least partially offset the one or more particular environmental conditions.

Abstract: Examples described herein involve detecting known environmental conditions of using a neural network. An example implementation involves a playback device receiving data indicating a response of a listening environment to audio output of one or more playback devices as captured by a microphone and determining an input vector for a neural network. The playback device provides the determined input vector to the neural network, which includes an output layer comprising neurons that correspond to respective environmental conditions. The playback device detects that the input vector caused one or more neurons of the neural network to fire such that the neural network indicates that one or more particular environmental conditions are present in the listening environment. The playback device adjusts audio output of the one or more playback devices to at least partially offset the one or more particular environmental conditions.

Abstract: Examples described herein involve detecting known environmental conditions of using a neural network. An example implementation involves a playback device receiving data indicating a response of a listening environment to audio output of one or more playback devices as captured by a microphone and determining an input vector for a neural network. The playback device provides the determined input vector to the neural network, which includes an output layer comprising neurons that correspond to respective environmental conditions. The playback device detects that the input vector caused one or more neurons of the neural network to fire such that the neural network indicates that one or more particular environmental conditions are present in the listening environment. The playback device adjusts audio output of the one or more playback devices to at least partially offset the one or more particular environmental conditions.

Abstract: Examples described herein involve detecting known impairments or other known conditions using a neural network. An example implementation receives a response matrix that represents, in respective dimensions, responses of a given playback device under respective iterations of a sound captured by a recording device, the iterations including first iterations with respective impairments to the recording device and second iterations without the respective impairments to the recording device. The implementation determines principle components representing the axes of greatest variance in the response matrix, a principle-component matrix that represents a given set of the principle components, and a teaching matrix by projecting the principle-component onto the response matrix.

Abstract: Examples described herein involve detecting known impairments or other known conditions using a neural network. An example implementation receives a response matrix that represents, in respective dimensions, responses of a given playback device under respective iterations of a sound captured by a recording device, the iterations including first iterations with respective impairments to the recording device and second iterations without the respective impairments to the recording device. The implementation determines principle components representing the axes of greatest variance in the response matrix, a principle-component matrix that represents a given set of the principle components, and a teaching matrix by projecting the principle-component onto the response matrix.

Abstract: Examples described herein involve detecting known impairments or other known conditions using a neural network. An example implementation involves receiving data indicating a response of a playback device as captured by a microphone. The implementation also involves determining an input vector by projecting a response vector that represents the response of the playback device onto a principle component matrix representing variance caused by one or more known impairments. The implementation further involves providing the determined input vector to a neural network that includes an output layer comprising neurons that correspond to respective known impairments.