Determining a virtual listening environment
One or more acoustic parameters of a current acoustic environment of a user may be determined based on sensor signals captured by one or more sensors of the device. One or more preset acoustic parameters may be determined based on the one or more acoustic parameters of the current acoustic environment of the user and an acoustic environment of an audio file comprising audio signals that is determined based on the audio signals of the audio file or metadata of the audio file. The audio signals may be spatially rendered by applying spatial filters that include the one or more preset acoustic parameters to the audio signals, resulting in binaural audio signals. The binaural audio signals may be used to drive speakers of a headset. Other aspects are described and claimed.
1 . A method, performed by a processor of a device, comprising:
determining one or more acoustic parameters of a current acoustic environment of a user, based on sensor signals captured by one or more sensors of the device;
determining one or more preset acoustic parameters based on both 1) the one or more acoustic parameters of the current acoustic environment of the user and 2) an acoustic environment of an audio file comprising audio signals, the acoustic environment of the audio file being determined based on the audio signals of the audio file or metadata of the audio file;
spatially rendering the audio signals by applying the one or more preset acoustic parameters to the audio signals, resulting in binaural audio signals; and
driving speakers with the binaural audio signals.
2 . The method of claim 1 , wherein determining the one or more preset acoustic parameters includes selecting the one or more preset acoustic parameters to increase resemblance to the acoustic environment of the audio file in response to the acoustic environment of the audio file being an outdoor scene.
3 . The method of claim 1 , wherein determining the one or more preset acoustic parameters includes selecting the one or more preset acoustic parameters to increase resemblance to the acoustic environment of the audio file in response to an indicator in the metadata.
4 . The method of claim 1 , wherein determining the one or more preset acoustic parameters includes selecting acoustic parameters that are specified in the metadata as the one or more preset acoustic parameters in response to the acoustic parameters being present or indicated by a control in the metadata.
5 . The method of claim 1 , wherein determining the one or more preset acoustic parameters includes selecting the one or more preset acoustic parameters to increase resemblance to the current acoustic environment of the user in response to the acoustic environment of the audio file being indoors or non-existent.
6 . The method of claim 1 , wherein determining the one or more preset acoustic parameters includes selecting the one or more preset acoustic parameters to increase resemblance to the acoustic environment of the audio file, in response to the audio file being associated with a visual work.
7 . The method of claim 1 , wherein determining the one or more preset acoustic parameters includes selecting the one or more preset acoustic parameters to increase resemblance to the current acoustic environment of the user in response to the audio file not being associated with a visual work.
8 . The method of claim 1 , wherein determining the one or more preset acoustic parameters includes classifying the acoustic environment of the audio file and selecting the one or more preset acoustic parameters as a balance between the acoustic environment of the audio file and the one or more acoustic parameters of a current acoustic environment of a user.
9 . The method of claim 1 , wherein determining the acoustic environment of the audio file includes extracting content-based acoustic parameters from the audio signals of the audio file or from the metadata.
10 . The method of claim 1 , wherein the acoustic environment of the audio file is classified as at least one of: a room volume, being in an open space, or being in an enclosed space.
11 . A system, comprising:
a microphone generating a microphone signal that characterizes an acoustic environment of the system; and
non-transitory computer-readable memory storing executable instructions and a processor configured to execute the instructions to cause the system to:
determine one or more acoustic parameters of the acoustic environment of the system, including at least a reverberation duration, based on the microphone signal;
determine one or more preset acoustic parameters based on both 1) the one or more acoustic parameters of the acoustic environment of the system and 2) an acoustic environment of an audio file that is determined based on audio signals of the audio file or metadata of the audio file;
spatially render the audio signals comprising applying the one or more preset acoustic parameters to the audio signals, resulting in spatialized audio signals; and
drive speakers with the spatialized audio signals.
12 . The system of claim 11 , wherein the system includes a headphone set on which the microphone and the speakers are integrated.
13 . The system of claim 11 , wherein the acoustic environment of the audio file is classified as a type of space, including: a room, a library, a cathedral, a stadium.
14 . The system of claim 11 , wherein determining the acoustic environment of an audio file is further based on a video signal associated with the audio file.
15 . The system of claim 11 , wherein determining the one or more acoustic parameters of the acoustic environment of the system or determining acoustic parameters based on the audio signals of the audio file are performed using a machine-learning model.
16 . The system of claim 11 , wherein determining the one or more acoustic parameters of the acoustic environment of the system or determining acoustic parameters based on the audio signals of the audio file are performed using a digital signal processing algorithm including at least one of a blind room estimation algorithm, beamforming, or a frequency domain adaptive filter (FDAF).
17 . The system of claim 11 , further comprising storing the one or more acoustic parameters of the acoustic environment of the system, and re-using the stored one or more acoustic parameters of current acoustic environment at a later time, in response to sensing the acoustic environment of the system at the later time.
18 . The system of claim 11 , wherein determining the one or more preset acoustic parameters is performed using a rule-based algorithm that includes a content-type, an audio scene type, and the one or more acoustic parameters of the acoustic environment of the system.
19 . The system of claim 11 , wherein determining the one or more preset acoustic parameters is performed using a machine learning model that includes a content-type, an audio scene type, and the one or more acoustic parameters of the acoustic environment of the system.
20 . The system of claim 11 , wherein the one or more acoustic parameters and the one or more preset acoustic parameters includes at least one of a reverberation time, a measure of reverberation time, a direct to reverberant ration (DRR), reflection density, envelopment, or speech clarity.