Examples of the disclosure describe systems and methods for estimating acoustic properties of an environment. In an example method, a first audio signal is received via a microphone of a wearable head device. An envelope of the first audio signal is determined, and a first reverberation time is estimated based on the envelope of the first audio signal. A difference between the first reverberation time and a second reverberation time is determined. A change in the environment is determined based on the difference between the first reverberation time and the second reverberation time. A second audio signal is presented via a speaker of a wearable head device, wherein the second audio signal is based on the second reverberation time.

An IoT Smart Sound Level Meter that is connected through the internet via ethernet cable, wifi or cellular network. The IoT Smart Sound Level Meter measures and monitors sound pressure levels. Sound pressure levels are measured in decibels. The sound pressure level data that is sent via the internet through ethernet, wifi or cellular network to cloud servers where the data is stored and saved. The cloud servers are connected to Application Programming Interfaces (API) connected to web-based software that converts the sound pressure level data into decibel measurements. These measurements are displayed to the end-user through the web-based software and/or application providing real-time decibel level measurements and historic sound pressure level recordings. The web-based software and/or application provides access to view decibel measurement data remotely from a computer, laptop, tablet, smart watch, or mobile device.

An IoT Smart Sound Level Meter that is connected through the internet via ethernet cable, wifi or cellular network. The IoT Smart Sound Level Meter measures and monitors sound pressure levels. Sound pressure levels are measured in decibels. The sound pressure level data that is sent via the internet through ethernet, wifi or cellular network to cloud servers where the data is stored and saved. The cloud servers are connected to Application Programming Interfaces (API) connected to web-based software that converts the sound pressure level data into decibel measurements. These measurements are displayed to the end-user through the web-based software and/or application providing real-time decibel level measurements and historic sound pressure level recordings. The web-based software and/or application provides access to view decibel measurement data remotely from a computer, laptop, tablet, smart watch, or mobile device.

A method comprises: receiving an image of a real-world environment; using a machine learning classifier, classifying the image to produce classifications associated with acoustic presets for an acoustic environment simulation, the acoustic presets each including acoustic parameters that represent sound reverberation; and selecting an acoustic preset among the acoustic presets based on the classifications.

A method comprises: receiving an image of a real-world environment; using a machine learning classifier, classifying the image to produce classifications associated with acoustic presets for an acoustic environment simulation, the acoustic presets each including acoustic parameters that represent sound reverberation; and selecting an acoustic preset among the acoustic presets based on the classifications.

Audio signal dereverberation can be carried out in accordance instructions on a machine readable storage medium, using a processor. In an example, a location of a person in a room can be determined. An audio signal received from the location of the person can be captured using beamforming. Room properties can be determined based in part on a signal sweep of the room. A dereverberation parameter can be determined based in part on the location of the person and the room properties. The dereverberation parameter can be applied to the audio signal.

Audio signal dereverberation can be carried out in accordance instructions on a machine readable storage medium, using a processor. In an example, a location of a person in a room can be determined. An audio signal received from the location of the person can be captured using beamforming. Room properties can be determined based in part on a signal sweep of the room. A dereverberation parameter can be determined based in part on the location of the person and the room properties. The dereverberation parameter can be applied to the audio signal.

Examples of the disclosure describe systems and methods for estimating acoustic properties of an environment. In an example method, a first audio signal is received via a microphone of a wearable head device. An envelope of the first audio signal is determined, and a first reverberation time is estimated based on the envelope of the first audio signal. A difference between the first reverberation time and a second reverberation time is determined. A change in the environment is determined based on the difference between the first reverberation time and the second reverberation time. A second audio signal is presented via a speaker of a wearable head device, wherein the second audio signal is based on the second reverberation time.

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.

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.