Abstract: 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.

Abstract: 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.

Abstract: In some embodiments, a method, comprises: dividing, using at least one processor, an audio input into speech and non-speech segments; for each frame in each non-speech segment, estimating, using the at least one processor, a time-varying noise spectrum of the non-speech segment; for each frame in each speech segment, estimating, using the at least one processor, speech spectrum of the speech segment; for each frame in each speech segment, identifying one or more non-speech frequency components in the speech spectrum; comparing the one or more non-speech frequency components with one or more corresponding frequency components in a plurality of estimated noise spectra and selecting the estimated noise spectrum from the plurality of estimated noise spectra based on a result of the comparing.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments are described for a method for localizing a set of speakers (106) and microphones (108), having only the times of arrival between each of the speakers and microphones. An autodiscovery process (107) 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.

Abstract: 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.

Abstract: Embodiments are described for a method for localizing a set of speakers (106) and microphones (108), having only the times of arrival between each of the speakers and microphones. An autodiscovery process (107) 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.