Techniques and systems are described for generating and using a sound localization model. A described technique includes obtaining for a building a sound sensor map indicating locations of first and second sound sensor devices in respective first and second rooms of the building; causing an autonomous device to navigate to the first room and to emit, during a time window, sound patterns at one or more frequencies within the first room; receiving sound data including first and second sound data respectively from the first and second sound sensor devices that are observed during the time window; and generating and storing a sound localization model based on the sound sensor map, autonomous device location information, and the received sound data, the model being configured to compensate for how sounds travels among rooms in at least a portion of the building such that an origin room of a sound source is identifiable.

There is provided a smart audio system including multiple audio devices and a central server. The central server confirms a model of every audio device and a position thereof in an operation area in a scan mode. The central server confirms a user position or a user state to accordingly control output power of a speaker of each of the multiple audio devices in an operation mode.

A method is provided that includes obtaining two or more microphone audio signals; analysing the two or more microphone audio signals for a defined noise type; and processing the two or more microphone audio signals based on the analysis to generate at least one audio signal suitable for automatic speech recognition. A corresponding apparatus is also provided.

Example embodiments may include one or more of receiving an electrical sound emission signal from a sound controller, interrupting reception of the electrical sound emission signal, by a sound emission interruption circuit connected to a sound emitter, and receiving an electrical ambient sound signal via a sound detection circuit, based on ambient sound sensed by the sound emitter when the reception of the electrical sound emission signal is interrupted by the sound emission interruption circuit.

A vehicle microphone system may include at least two microphones forming a microphone array, at least one loudspeaker configured to emit audio signals. a processor coupled to a memory and programmed to receive incoming audio signals from the microphone array, determine at least one parameter for each channel of the microphone array, determine at least one filter to apply to at least one channel based on a difference between the parameters of each channel, and store the at least one filter in the memory.

A microphone array system, comprises N microphones, including a first microphone . . . a Nth microphone, wherein N is a natural number greater than 2. Each of the N microphones is provided with: an acoustic transducer for picking up a sound signal and converting the sound signal into an electric signal; a voice activation detector, connected to a corresponding acoustic transducer, and configured to perform a voice activation detection on the electric signal and form an activation signal; a buffer memory, connected to the acoustic transducer, and configured to store a 1/N electric signal of a predetermined segment; a sound wire interface, connected to a corresponding acoustic transducer, the buffer memory, and the voice activation detector, wherein the sound wire interface is connected to an external master chip via a sound wire bus for outputting the activation signal to the external master chip.

Array microphone systems and methods that can automatically focus and/or place beamformed lobes in response to detected sound activity are provided. The automatic focus and/or placement of the beamformed lobes can be inhibited based on a remote far end audio signal. The quality of the coverage of audio sources in an environment may be improved by ensuring that beamformed lobes are optimally picking up the audio sources even if they have moved and changed locations.

A substantially full-range loudspeaker system for acoustic sound reinforcement can include a woofer section with a first loudspeaker driver configured to reproduce audio signals in a first frequency range and a controlled-directivity horn section configured to reproduce other audio signals in a different second frequency range. In an example, the horn section includes a second loudspeaker driver comprising a cone-diaphragm transducer and a dust dome, an acoustic lens having a first side and an opposite second side, wherein the first side of the lens is coupled to a sound-projecting face of the second loudspeaker driver, and a waveguide coupled to the second side of the lens, wherein the waveguide comprises walls that follow arcuate paths in horizontal and vertical planes.

Implementations set forth herein relate to an automated assistant that uses circumstantial condition data, generated based on circumstantial conditions of an input, to determine whether the input should affect an action been initialized by a particular user. The automated assistant can allow each user to manipulate their respective ongoing action without necessitating interruptions for soliciting explicit user authentication. For example, when an individual in a group of persons interacts with the automated assistant to initialize or affect a particular ongoing action, the automated assistant can generate data that correlates that individual to the particular ongoing action. The data can be generated using a variety of different input modalities, which can be dynamically selected based on changing circumstances of the individual. Therefore, different sets of input modalities can be processed each time a user provides an input for modifying an ongoing action and/or initializing another action.

A method and a system for speech enhancement including a time synchronization unit configured to synchronize microphone signals sent from at least two microphones; a source separation unit configured to separate the synchronized microphone signals and output a separated speech signal, which corresponds to a speech source; and a noise reduction unit including a feature extraction unit configured to extract a speech feature of the separated speech signal and a neural network configured to receive the speech feature and output a clean speech feature.