Disclosed is an apparatus for determining goodness of fit related to microphone placement capable of communicating with other electronic devices and an external server in a 5G communication network, in which an artificial intelligence (AI) algorithm and/or a machine learning algorithm are executed. The apparatus includes an inputter, a communicator, a storage, and a processor. As the apparatus is provided, sound recognition effects can be improved.

Various examples are provided for surveillance of an audio stream. In one example, a method includes identifying presence or absence of a sound type of interest at a location during a time period; selecting the sound type from a library of sound type information to provide a collection of sound type information; incorporating the collection on a device proximate to the location; acquiring an audio stream from the location by the device to provide a locational audio stream; analyzing the locational audio stream to determine whether a sound type in the collection is present in the audio stream; and generating a notification to a user or computer if a sound type in the collection is present. The device can acquire and process the audio stream. In another example, a bulk sound type information library can be generated by identifying sound types of interest including them based upon a confidence level.

Embodiments are disclosed for non-intrusive transducer health detection in an audio system. In an embodiment, a method performed by the audio system comprises outputting one or more encoded inaudible acoustic signals into an acoustic transmission medium using a first transducer. The one or more encoded inaudible acoustic signals are received from the acoustic transmission medium using a second transducer of the audio system. The received one or more encoded inaudible acoustic signals are used to identify failure or degradation of the first or second transducer.

In some embodiments, the electronic device includes a speaker, a microphone, a memory, a digital signal processor (DSP), a driver, and a processor. The processor is configured to: obtain a first sound signal by combining a first signal, a second signal, and a first anti-phase signal; extract, from a second sound signal related to the first sound signal, a first DPOAE signal; obtain a third sound signal by combining a fourth signal, a fifth signal, and a second anti-phase signal; extract, from a fourth sound signal related to the third sound signal, a second DPOAE signal; obtain a user hearing profile based on the first and second DPOAE signals; and perform, based on the user hearing profile, at least one of a sound volume change and an equalization (EQ) change of a sound to be output.

Disclosed herein, among other things, are systems and methods for a user adjustment interface using remote computing resources. Specifically, a system can include a mobile device in communication with a hearing assistance device or a remote server. The mobile device can interpret an acoustic environment and send information about the environment to a remote server. The remote server can determine and send information to the mobile device for use in a user interface. The mobile device can receive a user selection of hearing assistance parameter information to be sent to the hearing assistance device.

A controller in a wireless system may, in a first mode, communicate directly with a wireless microphone, via a first wireless protocol, in order to control the wireless microphone, and may, in a second mode, communicate using the first wireless protocol with a wireless access point, in order to control or configure the wireless microphone.

An acoustic feedback control adaptive method, the input signal being a function of a captured signal and an estimation of an acoustic feedback, the method including the following steps: —determining an impulse response (RI) of a filter (A) according to a partition of time blocks (b.sub.0, . . . b.sub.i, . . . , b.sub.Nb), according to the following steps of: —for each sub-block (h.sub.1,i, h.sub.2,i, . . . h.sub.j,i, . . . h.sub.Ni,i) of each block of the impulse response (RI), calculating a frequency transform (F.sub.1,i, F.sub.2,i, . . . F.sub.j,i, . . . F.sub.Ni,i); —repeating the following steps of: —applying the filter (A) to the output signal (u) using the frequency transform (F.sub.1,i, F.sub.2,i, . . . F.sub.j,i, . . . F.sub.Ni,i) of each sub-block (h.sub.1,i, h.sub.2,i, . . . h.sub.j,i, . . . h.sub.Ni,i); —updating the frequency transform (F.sub.1,i, F.sub.2,i, . . . F.sub.j,i, . . . F.sub.Ni,i) of each sub-block (h.sub.1,i, h.sub.2,i, . . . h.sub.j,i, . . . h.sub.Ni,i) as a function of the output signal and the input signal based on the same partition as that used in the step of applying the filter (A).

Disclosed herein, among other things, are systems and methods for a user adjustment interface using remote computing resources. Specifically, a system can include a mobile device in communication with a hearing assistance device or a remote server. The mobile device can interpret an acoustic environment and send information about the environment to a remote server. The remote server can determine and send information to the mobile device for use in a user interface. The mobile device can receive a user selection of hearing assistance parameter information to be sent to the hearing assistance device.

Systems, methods, and apparatus for corona detection using audio data are provided. In one example embodiment, the method includes obtaining, by one or more computing devices, audio data indicative of audio associated with an electrical system for at least one time interval. The method includes partitioning, by the one or more computing devices, the audio data for the time interval into a plurality of time windows. The method includes determining, by the one or more computing devices, a signal indicative of a presence of corona based at least in part on audio data collected within an identified time window of the plurality of time windows relative to audio data collected for a remainder of the time interval.

A method for guiding deep breathing may include receiving a request from a user to initiate a deep breathing exercise on a user-controlled device. The method may include monitoring deep breathing using one or more sensors on an ear-worn device in response to initiating the deep breathing exercise. Examples of sensors include at least one of a motion detector, a microphone, a heart rate sensor, and an electrophysiological sensor. The method may further include initiating an end to the deep breathing exercise. The method may be used with various hearing systems including an ear-worn device and optionally a user-controllable device, such as a smartphone.