An example method of operation may include identifying, in a particular room environment, a number of speakers and one or more microphones on a network controlled by a controller and amplifier, providing test signals to play sequentially from each amplifier channel of the amplifier and the speakers, monitoring the test signals from the one or more microphones simultaneously to detect operational speakers and amplifier channels, providing additional test signals to the speakers to determine tuning parameters, detecting the additional test signals at the one or more microphones controlled by the controller, and automatically establishing a background noise level and noise spectrum of the room environment based on the detected additional test signals.

A method, the method comprising retrieving a sound intensity map for a venue, wherein the sound intensity map is divided up into a plurality of regions, wherein the sound intensity map predicts a sound quality for each region during a current event. Receiving data from a plurality of IOT enabled operated aerial vehicles, where each IOT enabled operated aerial vehicle of the plurality of IOT enabled operated aerial vehicles travels around different regions of the plurality of regions, wherein each IOT enabled operated aerial vehicle collects data during the event. Comparing the received data to the sound intensity map to determine the region where an audio component of a venue audio needs to be adjusted. Determining the adjustment required for the audio component and adjusting the audio equipment.

A public address method for live broadcast, in a helmet, of an audio signal conditioned from a plurality of raw audio channels, includes a pre-processing phase including the operations that consist of taking into account characteristics of the auditory perception of the listener; correcting each channel as a function of the characteristics of the auditory perception of the listener; a mixing phase including the production, from the channels thus pre-processed, of a mixed audio signal; a post-processing phase including the operations that consist of: measuring the sound level of a background noise; correcting the mixed audio signal as a function of the sound level of the background noise; a phase of reproducing, in the helmet, the conditioned audio signal resulting from post-processing.

The present disclosure relates to an information processing apparatus and an information processing method as well as a program that make it possible to control, by a partition provided on a boundary between two spaces, a visual shielding property and an auditory shielding property of a first space to a person in a second space in an interlocking relationship with each other in response to a distance between the person in the second space and the partition. A distance between the partition, which partitions the first space and the second space, and a person in the second space is measured, and transmittance of the partition and magnitude of output of audio in the first space to the second space are controlled in response to the measured distance. The present disclosure can be applied to a control apparatus for a partition section.

A noise estimation method including steps of generating gap confidence values in response to microphone output and playback signals, and using the gap confidence values to generate an estimate of background noise in a playback environment. Each gap confidence value is indicative of confidence of presence of a gap at a corresponding time in the playback signal, and may be a combination of candidate noise estimates weighted by the gap confidence values. Generation of the candidate noise estimates may but need not include performance of echo cancellation. Optionally, noise compensation is performed on an audio input signal using the generated background noise estimate. Other aspects are systems configured to perform any embodiment of the noise estimation method.

Systems and methods for optimizing network microphone devices using noise classification are disclosed herein. In one example, individual microphones of a network microphone device (NMD) detect sound. The sound data is analyzed to detect a trigger event such as a wake word. Metadata associated with the sound data is captured in a lookback buffer of the NMD. After detecting the trigger event, the metadata is analyzed to classify noise in the sound data. Based on the classified noise, at least one performance parameter of the NMD is modified.

A pervasive listening method including steps of inserting at least one forced gap in a playback signal (thus generating a modified playback signal), and during playback of the modified playback signal, monitoring non-playback content (e.g., including by generating an estimate of background noise) in a playback environment using output of a microphone in the playback environment. Optionally, the method includes generation of the playback signal, including by processing of (e.g., performing noise compensation on) an input signal using a result (e.g., a background noise estimate) of the monitoring of non-playback content. Other aspects are systems configured to perform any embodiment of the pervasive listening method.

Systems and methods for optimizing voice detection via a network microphone device (NMD) based on a selected voice-assistant service (VAS) are disclosed herein. In one example, the NMD detects sound via individual microphones and selects a first VAS to communicate with the NMD. The NMD produces a first sound-data stream based on the detected sound using a spatial processor in a first configuration. Once the NMD determines that a second VAS is to be selected over the first VAS, the spatial processor assumes a second configuration for producing a second sound-data stream based on the detected sound. The second sound-data stream is then transmitted to one or more remote computing devices associated with the second VAS.

Example techniques facilitate calibration of a playback device. An example implementation involves a computing device capturing, via a microphone, data representing multiple iterations of a calibration sound as played by a playback device. The computing device identifies multiple sections within the captured data. Two or more sections represent respective iterations of the calibration sound as played by the playback device. Based on the multiple identified sections, the computing device determines a frequency response of the playback device, the frequency response of the playback device representing audio output by the playback device and acoustic characteristics of an environment around the playback device. Based on the frequency response of the playback device and a target frequency response, the computing device determines one or more parameters of an audio processing algorithm and sends, to the playback device, the one or more parameters of the audio processing algorithm.

A noise estimation method including steps of generating gap confidence values in response to microphone output and playback signals, and using the gap confidence values to generate an estimate of background noise in a playback environment. Each gap confidence value is indicative of confidence of presence of a gap at a corresponding time in the playback signal, and may be a combination of candidate noise estimates weighted by the gap confidence values. Generation of the candidate noise estimates may but need not include performance of echo cancellation. Optionally, noise compensation is performed on an audio input signal using the generated background noise estimate. Other aspects are systems configured to perform any embodiment of the noise estimation method.