Abstract: Some noise compensation methods involve receiving microphone signals corresponding to ambient noise from a noise source location in or near an audio environment, determining or estimating a listener position in the audio environment and estimating at least one critical distance, which is a distance from the noise source location at which directly propagated sound pressure is equal to diffuse field sound pressure. Some examples involve estimating whether the listener position is within the at least one critical distance and implementing a noise compensation method for the ambient noise based, at least in part, on an estimate of whether the listener position is within the critical distance.

Abstract: Noise compensation method comprising: (a) receiving a content stream including content audio data; (b) receiving first microphone signals from a first device; (c) detecting ambient noise from a noise source location in or near the audio environment; (d) causing a first wireless signal to be transmitted from the first device to a second device, the first wireless signal including instructions for the second device to record an audio segment (e) receiving a second wireless signal from the second device; (f) determining a content stream audio segment time interval for a content stream audio segment; (g) receiving a third wireless signal from the second device, including a recorded audio segment captured via a second device microphone; (h) determining a second device ambient noise signal at the second device location; and (i) implementing a noise compensation method for the content audio data based, at least in part, on the second device ambient noise signal.

Abstract: An apparatus and method of loudspeaker equalization. The method combines default tunings (generated based on a default listening environment) and room tunings (generated based on an end user listening environment) to result in combined tunings that account for differences between the end user listening environment and the default listening environment.

Abstract: Noise compensation method comprising: (a) receiving a content stream including content audio data; (b) receiving first microphone signals from a first device; (c) detecting ambient noise from a noise source location in or near the audio environment; (d) causing a first wireless signal to be transmitted from the first device to a second device, the first wireless signal including instructions for the second device to record an audio segment (e) receiving a second wireless signal from the second device; (f) determining a content stream audio segment time interval for a content stream audio segment; (g) receiving a third wireless signal from the second device, including a recorded audio segment captured via a second device microphone; (h) determining a second device ambient noise signal at the second device location; and (i) implementing a noise compensation method for the content audio data based, at least in part, on the second device ambient noise signal.

Abstract: Some noise compensation methods involve receiving microphone signals corresponding to ambient noise from a noise source location in or near an audio environment, determining or estimating a listener position in the audio environment and estimating at least one critical distance, which is a distance from the noise source location at which directly propagated sound pressure is equal to diffuse field sound pressure. Some examples involve estimating whether the listener position is within the at least one critical distance and implementing a noise compensation method for the ambient noise based, at least in part, on an estimate of whether the listener position is within the critical distance.

Abstract: An apparatus and method of loudspeaker equalization. The method combines default tunings (generated based on a default listening environment) and room tunings (generated based on an end user listening environment) to result in combined tunings that account for differences between the end user listening environment and the default listening environment.

Abstract: In some embodiments, a method for benchmarking an audio processing algorithm (“APA”) while the APA is executed in a manner simulating expected real time execution by a deployed system. Other embodiments include a method including steps of determining a synthetic APA which corresponds to a counterpart APA (intended for real use by a first deployed system), and benchmarking the synthetic APA while it is executed in a manner simulating expected real time execution of the synthetic APA by a contemplated deployed system. Other aspects include a system or device configured to implement any embodiment of the inventive method, or including a memory which stores data indicative of at least one synthetic APA determined in accordance with, or a benchmark generated by, an embodiment of the inventive method or steps thereof, and a computer readable medium which stores code for implementing any embodiment of the inventive method or steps thereof.

Abstract: In some embodiments, a method for benchmarking an audio processing algorithm (“APA”) while the APA is executed in a manner simulating expected real time execution by a deployed system. Other embodiments include a method including steps of determining a synthetic APA which corresponds to a counterpart APA (intended for real use by a first deployed system), and benchmarking the synthetic APA while it is executed in a manner simulating expected real time execution of the synthetic APA by a contemplated deployed system. Other aspects include a system or device configured to implement any embodiment of the inventive method, or including a memory which stores data indicative of at least one synthetic APA determined in accordance with, or a benchmark generated by, an embodiment of the inventive method or steps thereof, and a computer readable medium which stores code for implementing any embodiment of the inventive method or steps thereof.

Abstract: Systems and methods for leveling loudness variation in an audio signal are described. Embodiments use both a perceptual leveling algorithm and a standards-based loudness measure together to minimize audio process artifacts and ensure that the measured loudness of the processed audio is close to a required measure, according to a particular standard measurement of loudness. These systems and methods can be used either offline or in real-time.

Abstract: Systems and methods for leveling loudness variation in an audio signal are described. Embodiments use both a perceptual leveling algorithm and a standards-based loudness measure together to minimize audio process artifacts and ensure that the measured loudness of the processed audio is close to a required measure, according to a particular standard measurement of loudness. These systems and methods can be used either offline or in real-time.

Abstract: Systems and methods for leveling loudness variation in an audio signal are described. Embodiments use both a perceptual leveling algorithm and a standards-based loudness measure together to minimize audio process artifacts and ensure that the measured loudness of the processed audio is close to a required measure, according to a particular standard measurement of loudness. These systems and methods can be used either offline or in real-time.

Abstract: Systems and methods for leveling loudness variation in an audio signal are described. Embodiments use both a perceptual leveling algorithm and a standards-based loudness measure together to minimize audio process artifacts and ensure that the measured loudness of the processed audio is close to a required measure, according to a particular standard measurement of loudness. These systems and methods can be used either offline or in real-time.

Abstract: Systems and methods for leveling loudness variation in an audio signal are described. Embodiments use both a perceptual leveling algorithm and a standards-based loudness measure together to minimize audio process artifacts and ensure that the measured loudness of the processed audio is close to a required measure, according to a particular standard measurement of loudness. These systems and methods can be used either offline or in real-time.

Abstract: Systems and methods for leveling loudness variation in an audio signal are described. Embodiments use both a perceptual leveling algorithm and a standards-based loudness measure together to minimize audio process artifacts and ensure that the measured loudness of the processed audio is close to a required measure, according to a particular standard measurement of loudness. These systems and methods can be used either offline or in real-time.