A vehicle that selectively provides important sound to an occupant includes: a microphone configured to receive a noise sound; a speaker; a storage configured to store a plurality of sound waveforms and a warning sound source corresponding to each sound waveform of the plurality of sound waveforms; and a controller configured to generate a noise canceling signal based on the noise sound, control the speaker to output a noise canceling sound corresponding to the noise canceling signal, compare a waveform of the noise sound with the plurality of sound waveforms when a sound pressure level of the noise sound is greater than a threshold value, and when the waveform of the noise sound matches any sound waveform of the plurality of sound waveforms, and control the speaker to play a warning sound source corresponding to the sound waveform matching the noise sound.

A sound generation apparatus includes sound collection means configured to collect a sound of a sound source in a space, image capture means configured to capture an image of the sound source, estimation means configured to estimate an attribute of the sound source from the image captured by the image capture means, sound generation means configured to obtain an acoustic characteristic of a target sound included in the sound collected by the sound collection means and to generate multiple masking sounds on the basis of the acoustic characteristic and the attribute of the sound source estimated by the estimation means, display means configured to display the attribute of the sound source estimated by the estimation means, sound selection means configured to receive selection of a masking sound from the masking sounds generated by the sound generation means, and sound output means configured to output the selected masking sound.

Utterances spoken or sung by a first person can be received, in real time. The detected utterances can be compared to at least a stored sample of utterances spoken or sung by the first person. Based on the comparing, audio of the utterances spoken or sung by the first person can be isolated from a background noise. A volume of the utterances spoken or sung by a first person relative to the background noise can be determined. A key indicator that indicates the volume of the detected utterances spoken or sung by the first person relative to the background noise can be generated. Based on the key indicator, information indicating the volume of the detected utterances spoken or sung by the first person relative to the background noise can be communicated.

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.

The amount of far-field noise transmitted by a primary communication device in an open-plan office environment is reduced by defining an acoustic perimeter of reference microphones around the primary device. Reference microphones generate a reference audio input including far-field noise in the proximity of the primary device. The primary device generates a main audio input including the voice of the primary speaker as well as background noise. Reference audio input is compared to main audio input to identify the background noise portion of the main audio signal. A noise reduction algorithm suppresses the identified background noise in the main audio signal. The one or more reference microphones defining the acoustic perimeter may be included in separate microphone devices placed in proximity to the main desktop phone, microphones within other nearby desktop telephone devices, or a combination of both types of devices.

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.

An example method of operation may include applying a set of initial power and gain parameters for a speaker, playing a stimulus signal via the speaker, determining a sound level at a microphone location and a sound level at a predefined distance from the speakers, determining a gain at the microphone location based on a difference of the sound level at the microphone location and the sound level at the predefined distance from the speaker, and applying the gain to the speaker output.

Described herein is an audio device with a microphone which may adapt the audio output volume of a speaker by either increasing or decreasing output volume based on an audio input volume from a user and a distance from the user to the audio device. The audio device may also adapt its output volume to lower the audio output based on detecting one or more interruptions including occupancy and acoustic sounds.

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.

An example method of operation may include identifying speakers and microphones connected to a network controlled by a controller, assigning a preliminary output gain to the speakers used to apply test signals, measuring ambient noise detected from the microphones, recording chirp responses from all microphones simultaneously based on the test signals, deconvolving all chirp responses to determine a corresponding number of impulse responses, and measuring average sound pressure levels (SPLs) of each of the microphones to obtain a SPL level based on an average of the SPLs.