A wireless sound-emitting device includes a housing adapted to be coupled to a wall at a source of electric power, a loudspeaker positioned at a periphery of the housing, a control module outputting an electric audio signal to the at least one loudspeaker, and a wireless communications module in electrical communication with the control module. The loudspeaker emits acoustic signals in a direction parallel to the wall, when the housing is coupled to the wall, with the acoustic signals reflecting off the wall. The device may produce a sound masking noise or play a sound recorded on an internal memory. The device may include an electric plug or be adapted to replace an electric outlet faceplate. The device may have electric pass-through outlets and may be powered by the source of electric power. The device may be controlled remotely, for example via an Internet of Things (IoT) platform.

Methods and apparatuses for addressing open space noise are disclosed. In one example, a method for masking open space noise includes optimizing a recorded naturally occurring sound signal. The method also includes outputting the optimized recorded naturally occurring sound signal from a plurality of speakers distributed in the open space. Further, the method includes displaying a natural system complementing the optimized recorded naturally occurring sound signal.

A sound state estimating unit detects surrounding sound at a timing at which a notification to a destination user occurs. A user state estimating unit detects a position of the destination user and positions of users other than the destination user at the timing at which the notification occurs. An output control unit controls output of the notification to the destination user at a timing at which it is determined that the surrounding sound detected by the sound state estimating unit is masking possible sound which can be used for masking in a case where the position of the destination user detected by the user state estimating unit is within a predetermined area.

A sound masking system according to the invention is disclosed in which one or more sound masking loudspeaker assemblies are coupled to one or more electronic sound masking signal generators. The loudspeaker assemblies in the system of the invention have a low directivity index and preferably emit an acoustic sound masking signal that has a sound masking spectrum specifically designed to provide superior sound masking in an open plan office. Each of the plurality of loudspeaker assemblies is oriented to provide the acoustic sound masking signal in a direct path into the predetermined area in which masking sound is needed. In addition, the sound masking system of the invention can include a remote control function by which a user can select from a plurality of stored sets of information for providing from a recipient loudspeaker assembly an acoustic sound masking signal having a-selected sound masking spectrum.

A portable sound-masking device for placing on a roll-up door having a number of horizontally-extending elements. The sound-masking device has a fixture, a magnet, and a transducer. The fixture has an inner face, an outer face, and a pair of spaced-apart tabs protruding from the inner face, the tabs being configured to engage one of the horizontally-extending elements of the roll-up door. The magnet is fastened to the inner face of the fixture and provides a force that tethers the sound-masking device to the roll-up door. The transducer is fastened to the outer face and is configured to emit sound-masking noise.

Methods and apparatuses for noise management are disclosed. In one example, a method includes receiving a plurality of noise level measurements. The method includes receiving a plurality of location data. In one example, the method further includes adjusting an environmental parameter utilizing the noise level measurements. In one example, the method further includes providing location services to a user directing the user to a geographical area having a lower noise level.

According to an example, a method comprises receiving a first audio stream from an input device, detecting presence within the first audio stream of at least an acoustic pattern, executing at least one corrective action over a portion of data of the first audio stream including the acoustic pattern such that a second audio stream is obtained, and transmitting the second audio stream to an output device.

The invention present disclosure relates to a method for preventing the voice of at least one person in a vehicle from being intercepted. At least one loudspeaker in the vehicle is activated, wherein the at least one loudspeaker is used to generate audio signals which have at least one frequency that is higher than the threshold frequency for sound that is audible to the at least one person, and the audio signals are used to interfere with the reception of a signal by means of at least one microphone when the at least one person uses their voice to articulate.

Methods and apparatuses for addressing open space noise are disclosed. In one example, a method for masking open space noise includes outputting from a speaker a speaker sound corresponding to a flow of water, and displaying a water element system, the water element system generating a sound of flowing water.

A method of phrase extraction for ASR models includes obtaining audio data characterizing an utterance and a corresponding ground-truth transcription of the utterance and modifying the audio data to obfuscate a particular phrase recited in the utterance. The method also includes processing, using a trained ASR model, the modified audio data to generate a predicted transcription of the utterance, and determining whether the predicted transcription includes the particular phrase by comparing the predicted transcription of the utterance to the ground-truth transcription of the utterance. When the predicted transcription includes the particular phrase, the method includes generating an output indicating that the trained ASR model leaked the particular phrase from a training data set used to train the ASR model.