Dolby International Ab

Dolby Laboratories Licensing Corporation

Davide Scaini

Giulio Cengarle

Daniel Arteaga

Antonio Mateos Sole

Alan Jeffrey Seefeldt

Mark R P Thomas

Avery Bruni

Olha Michelle Townsend

Christopher Graham Hines

David Matthew Fischer

Benjamin John Southwell

C Phillip Brown

David Gunawan

Suraj Suhas Barkale

Antonio Mateos Solé

Nicholas Laurence Engel

Patrick Winfrey Scannell

A method may involve: receiving direction of arrival (DOA) data corresponding to sound emitted by at least a first smart audio device of the audio environment that includes a first audio transmitter and a first audio receiver, the DOA data corresponding to sound received by at least a second smart audio device of the audio environment that includes a second audio transmitter and a second audio receiver, the DOA data corresponding to sound emitted by at least the second smart audio device and received by at least the first smart audio device; receiving one or more configuration parameters corresponding to the audio environment, to one or more audio devices, or both; and minimizing a cost function based at least in part on the DOA data and the configuration parameter(s), to estimate a position and an orientation of at least the first smart audio device and the second smart audio device.

Automatic localization of audio devices

Some methods may involve receiving a first content stream that includes first audio signals, rendering the first audio signals to produce first audio playback signals, generating first calibration signals, generating first modified audio playback signals by inserting the first calibration signals into the first audio playback signals, and causing a loudspeaker system to play back the first modified audio playback signals, to generate first audio device playback sound. The method(s) may involve receiving microphone signals corresponding to at least the first audio device playback sound and to second through Nth audio device playback sound corresponding to second through Nth modified audio playback signals (including second through Nth calibration signals) played back by second through Nth audio devices, extracting second through Nth calibration signals from the microphone signals and estimating at least one acoustic scene metric based, at least partly, on the second through Nth calibration signals.

Pervasive acoustic mapping

Some methods involve causing a plurality of audio devices in an audio environment to reproduce audio data, each audio device of the plurality of audio devices including at least one loudspeaker and at least one microphone, determining audio device location data including an audio device location for each audio device of the plurality of audio devices and obtaining microphone data from each audio device of the plurality of audio devices. Some methods involve determining a mutual audibility for each audio device of the plurality of audio devices relative to each other audio device of the plurality of audio devices, determining a user location of a person in the audio environment, determining a user location audibility of each audio device of the plurality of audio devices at the user location and controlling one or more aspects of audio device playback based, at least in part, on the user location audibility.

Audibility at user location through mutual device audibility

In an embodiment, a method comprises: filtering reference audio content items to separate the reference audio content items into different frequency bands; for each frequency band, extracting a first feature vector from at least a portion of each of the reference audio content items, wherein the first feature vector includes at least one audio characteristic of the reference audio content items; obtaining at least one semantic label from at least a portion of each of the reference audio content items; obtaining a second feature vector consisting of the first feature vectors per frequency band and the at least one semantic label; generating, based on the second feature vector, cluster feature vectors representing centroids of clusters; separating the reference audio content items according to the cluster feature vectors; and computing an average target profile for each cluster based on the reference audio content items in the cluster.

Automatic generation and selection of target profiles for dynamic equalization of audio content

Embodiments are disclosed for noise floor estimation and noise reduction, In an embodiment, a method comprises: obtaining an audio signal; dividing the audio signal into a plurality of buffers; determining time-frequency samples for each buffer of the audio signal; for each buffer and for each frequency, determining a median (or mean) and a measure of an amount of variation of energy based on the samples in the buffer and samples in neighboring buffers that together span a specified time range of the audio signal; combining the median (or mean) and the measure of the amount of variation of energy into a cost function; for each frequency: determining a signal energy of a particular buffer of the audio signal that corresponds to a minimum value of the cost function; selecting the signal energy as the estimated noise floor of the audio signal; and reducing, using the estimated noise floor, noise in the audio signal.

Noise floor estimation and noise reduction

Methods, systems, and computer program products that provide streaming capabilities to audio input and output devices are disclosed. An audio processing device connects an upstream device to a downstream device. The upstream device is plugged into an input port of the audio processing device. The audio processing device intercepts a signal from the upstream device to the downstream device. The audio processing device converts the signal to digital data and streams the digital data to a server. The digital data can include metadata, e.g., an input gain. The audio processing device can adjust the input gain in response to instructions from the server. The audio processing device feeds a pass-through copy of the audio signal to an output port. A user can connect the downstream device in a usual signal chain into the output port of the audio processing device. The streaming does not affect the user's workflow.

Audio input and output device with streaming capabilities

Embodiments are described for a method of simultaneously localizing a set of speakers and microphones, having only the times of arrival between each of the speakers and microphones. An autodiscovery process uses an external input to set: a global translation (3 continuous parameters), a global rotation (3 continuous parameters), and discrete symmetries, i.e., an exchange of any axis pairs and/or reversal of any axis. Different time of arrival acquisition techniques may be used, such as ultrasonic sweeps or generic multitrack audio content. The autodiscovery algorithm is based in minimizing a certain cost function, and the process allows for latencies in the recordings, possibly linked to the latencies in the emission.

Automatic discovery and localization of speaker locations in surround sound systems

Embodiments are described for a method for localizing a set of speakers () and microphones (), having only the times of arrival between each of the speakers and microphones. An autodiscovery process () uses an external input to set: a global translation (3 continuous parameters), a global rotation (3 continuous parameters), and discrete symmetries, i.e., an exchange of any axis pairs and/or reversal of any axis. Different time of arrival acquisition techniques may be used, such as ultrasonic sweeps or generic multitrack audio content. The autodiscovery algorithm is based in minimizing a certain cost function, and the process allows for latencies in the recordings, possibly linked to the latencies in the emission.

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Barcelona, Spain

Davide Scaini