An audio analysis system receives a first recording of a speech signal from an origin audio assembly and a second recording of at least a portion of the speech signal from a receiving audio assembly. The speech signal originates from a speaking user of the origin audio assembly and the second recording is recorded by a receiving audio assembly operated by a different user. Both the origin audio assembly and the receiving audio assembly are located within a room. The audio analysis system selects one or more audio frames in the first recording and one or more audio frames in the second recording that both occur over the same time period. The audio analysis system determines a transfer function for the room based in part on the selected one or more audio frames in the first recording and the selected one or more audio frames in the second recording.

An audio analysis system receives a first recording of a speech signal from an origin audio assembly and a second recording of at least a portion of the speech signal from a receiving audio assembly. The speech signal originates from a speaking user of the origin audio assembly and the second recording is recorded by a receiving audio assembly operated by a different user. Both the origin audio assembly and the receiving audio assembly are located within a room. The audio analysis system selects one or more audio frames in the first recording and one or more audio frames in the second recording that both occur over the same time period. The audio analysis system determines a transfer function for the room based in part on the selected one or more audio frames in the first recording and the selected one or more audio frames in the second recording.

A multichannel microphone-based reverberation time estimation method and device which use a deep neural network (DNN) are disclosed. A multichannel microphone-based reverberation time estimation method using a DNN, according to one embodiment, comprises the steps of: receiving a voice signal through a multichannel microphone; deriving a feature vector including spatial information by using the inputted voice signal; and estimating the degree of reverberation by applying the feature vector to the DNN.

A multichannel microphone-based reverberation time estimation method and device which use a deep neural network (DNN) are disclosed. A multichannel microphone-based reverberation time estimation method using a DNN, according to one embodiment, comprises the steps of: receiving a voice signal through a multichannel microphone; deriving a feature vector including spatial information by using the inputted voice signal; and estimating the degree of reverberation by applying the feature vector to the DNN.

Methods and systems are provided for determining a maximum expected response of a reverberant wavefield system to an excitation, where there is uncertainty in both the excitation and the dynamic properties of the system. An exemplary method of characterizing a reverberant response associated with an reverberant subsystem involves determining a first variance associated with an excitation energy exposed to the reverberant subsystem, determining a second variance associated with an effective damping loss factor of the reverberant subsystem, determining a third variance associated with an input modal power acceptance of the reverberant subsystemand for multiple connected subsystems, determining a fourth variance associated with a coupling loss factor of the coupled subsystemsdetermining a cumulative variance associated with the reverberant response based on the first variance, the second variance, and the third variance, and displaying an output influenced by the cumulative variance on a display device.

Aspects of the present disclosure relate to techniques for adjustment of room sound levels, comprising; driving a speaker with a known waveform, detecting a sound wave from the speaker with at least two microphones wherein the at least two microphones are configured in a known orientation, utilizing the known waveform and the sound wave detected by the at least two microphones and the known orientation of the at least two microphones to generate a room sound dynamic; applying a filter to adjust a sound level to account for the room sound dynamic. The room sound dynamic may be the speaker layout of the room, the room impulse response, the distance or angle of each speaker from the center of the room or other physical constraints that may affect the user's perception of sound coming from a sound system.

An image of at least a portion of a head of a user is received. A geometry is generated of the head wearing an eyewear device based in part on the received image of the head and a geometry of the eyewear device. The geometry of the eyewear device includes a microphone array composed of a plurality of acoustic sensors that are configured to detect sounds within a local area surrounding the microphone array. A simulation is performed of sound propagation between an audio source and the plurality of acoustic sensors based on the generated geometry. An acoustic transfer function (ATF) is determined associated with the microphone array based on the simulation. The determined ATF is customized to the user, and is provided to the eyewear device of the user.

A method for processing an audio signal in accordance with a room impulse response is described. The audio signal is separately processed with an early part and a late reverberation of the room impulse response, and the processed early part of the audio signal and the reverberated signal are combined. A transition from the early part to the late reverberation in the room impulse response is reached when a correlation measure reaches a threshold, the threshold being set dependent on the correlation measure for a selected one of the early reflections in the early part of the room impulse response.

Systems, methods, and apparatus are provided for monitoring and improving one or more acoustic parameters in single- and multi-zone habitable environments. The acoustic monitoring system includes a built structure, a central control circuit, an acoustic control system, an environment database, an electronic user device, and acoustic sensor arrays which are installed within the built structure. To facilitate the sensor installation process, the built structure may be delineated into one or more zones. The central control circuit may be configured to instruct the installation of acoustic sensor arrays in particular zones within the built structure to obtain improved or even optimal or near optimal acoustic sensor array placement.

Systems, methods, and apparatus are provided for monitoring and improving one or more acoustic parameters in single- and multi-zone habitable environments. The acoustic monitoring system includes a built structure, a central control circuit, an acoustic control system, an environment database, an electronic user device, and acoustic sensor arrays which are installed within the built structure. To facilitate the sensor installation process, the built structure may be delineated into one or more zones. The central control circuit may be configured to instruct the installation of acoustic sensor arrays in particular zones within the built structure to obtain improved or even optimal or near optimal acoustic sensor array placement.