Abstract:
Methods, systems, and devices are described for assessing the quality of end-to-end connectivity for a wireless communication device. Data generated from at least one of existing traffic and networking operations caused by existing traffic of the wireless communication device may be monitored to obtain information related to connectivity quality. One or more values of one or more metrics may be determined using the obtained information. The quality of end-to-end connectivity for the wireless communication device may be assessed using the value(s) of the metric(s). Based at least in part on a result of the assessment, an action may be performed to improve connectivity quality for the wireless communication.
Abstract:
An electronic device for suppressing noise in an audio signal is described. The electronic device includes a processor and instructions stored in memory. The electronic device receives an input audio signal and computes an overall noise estimate based on a stationary noise estimate, a non-stationary noise estimate and an excess noise estimate. The electronic device also computes an adaptive factor based on an input Signal-to-Noise Ratio (SNR) and one or more SNR limits. A set of gains is also computed using a spectral expansion gain function. The spectral expansion gain function is based on the overall noise estimate and the adaptive factor. The electronic device also applies the set of gains to the input audio signal to produce a noise-suppressed audio signal and provides the noise-suppressed audio signal.
Abstract:
A method for suppressing ambient noise using multiple audio signals may include providing at least two audio signals captured by at least two electro-acoustic transducers. The at least two audio signals may include desired audio and ambient noise. The method may also include performing beamforming on the at least two audio signals in order to obtain a desired audio reference signal that is separate from a noise reference signal.
Abstract:
In general, techniques are described for limiting active noise cancellation output. As one example, an apparatus comprising one or more processors may perform the techniques. The one or more processors may be configured to, when an estimated noise level increases, dynamically lowering application of active noise cancellation to at least a portion of an audio signal to obtain at least a portion of an active noise cancelled version of the audio signal.
Abstract:
A mechanism is provided that monitors secondary microphone signals, in a multi-microphone mobile device, to warn the user if one or more secondary microphones are covered while the mobile device is in use. In one example, smoothly averaged power estimates of the secondary microphones may be computed and compared against the noise floor estimate of a primary microphone. Microphone covering detection may be made by comparing the secondary microphone smooth power estimates to the noise floor estimate for the primary microphone. In another example, the noise floor estimates for the primary and secondary microphone signals may be compared to the difference in the sensitivity of the first and second microphones to determine if the secondary microphone is covered. Once detection is made, a warning signal may be generated and issued to the user.
Abstract:
A mobile audio device (for example, a cellular telephone, personal digital audio player, or MP3 player) performs Audio Dynamic Range Control (ADRC) (125) and Automatic Volume Control (AVC) (126) to increase the volume of sound (127) emitted from a speaker of' the mobile audio device so that faint passages of the audio will be more audible. This amplification of faint passages occurs without overly amplifying other louder passages, and without substantial distortion due to clipping. Multi-Microphone Active Noise Cancellation (MMANC) (133) functionality is, for example, used to remove background noise from audio information picked up on microphones of the mobile audio device. The noise-canceled audio may then be communicated from the device. The MMANC functionality generates a noise reference signal as an intermediate signal. The intermediate signal is conditioned and then used as a reference by the AVC process. The gain applied during the AVC process is a function of the noise reference signal.
Abstract:
Signal processing solutions take advantage of microphones located on different devices and improve the quality of transmitted voice signals in a communication system. With usage of various devices such as Bluetooth headsets, wired headsets and the like in conjunction with mobile handsets, multiple microphones located on different devices are exploited for improving performance and/or voice quality in a communication system. Audio signals are recorded by microphones on different devices and processed to produce various benefits, such as improved voice quality, background noise reduction, voice activity detection and the like.
Abstract:
Sound signal reception is improved by utilizing a plurality of microphones to capture sound signals which are then weighed to dynamically adjust signal quality. A first sound signal and a second sound signal are obtained from first and second microphones, respectively, where the first and second sound signals originate from one or more sound sources. A first signal characteristic (e.g., signal power, signal signal-to-noise ratio, etc.) is obtained for the first sound signal and a second signal characteristic is obtained for the second sound signal. The first and second sound signals are weighed or scaled based on their respective first and second signal characteristics. The weighed first and second sound signals are then combined to obtain an output sound signal.
Abstract:
Voice activity detection using multiple microphones can be based on a relationship between an energy at each of a speech reference microphone and a noise reference microphone. The energy output from each of the speech reference microphone and the noise reference microphone can be determined. A speech to noise energy ratio can be determined and compared to a predetermined voice activity threshold. In another embodiment, the absolute value of the autocorrelation of the speech and noise reference signals are determined and a ratio based on autocorrelation values is determined. Ratios that exceed the predetermined threshold can indicate the presence of a voice signal. The speech and noise energies or autocorrelations can be determined using a weighted average or over a discrete frame size.
Abstract:
This disclosure describes signal processing techniques that can improve the performance of blind source separation (BSS) techniques. In particular, the described techniques propose pre-processing steps that can help to de-correlate the different signals from one another prior to execution of the BSS techniques. In addition, the described techniques also propose optional post-processing steps that can further de-correlate the different signals following execution of the BSS techniques. The techniques may be particularly useful for improving BSS performance with highly correlated audio signals, e.g., from two microphones that are in close spatial proximity to one another.