Abstract: In one embodiment, an acoustic echo control (AEC) module receives an outgoing signal and an incoming signal, which, at various times, contains acoustic echo corresponding to the outgoing signal. The AEC module has a delay estimation block that estimates, in the time domain, the echo delay using an adaptive filtering technique. This delay estimation is used to align samples of the incoming signal having acoustic echo with the corresponding samples of the outgoing signal from which the acoustic echo originated. The AEC module determines whether or not samples of the incoming signal contain acoustic echo based on the aligned outgoing signal, and the determinations are applied to a hangover counter. The AEC module then suppresses acoustic echo in the incoming signal and adds comfort noise to the incoming signal. The amount of echo suppression performed is gradually increased or decreased based on comparisons of the counter to a hangover threshold.

Abstract: Superresolution image processing that can be applied when two image frames of the same scene are available so that image information from one frame can be used to enhance the image from the other frame. The superresolution image processing uses a sparse matrix generated based on a Markov random field defined over these two image frames. The sparse matrix is inverted and applied to the image data from the image frame that is being enhanced to generate a corresponding enhanced image.

Abstract: An adaptive filter configured to use multiple algorithm species that differ in the quality of echo suppression and respective burdens imposed on the computational resources of the host communication device. Depending on the available computational budget, the adaptive filter selects an algorithm species that, while supporting a relatively high quality of echo suppression, involves a relatively low risk of overwhelming the computational resources. The adaptive filter monitors changes in the available computational budget and, if appropriate or necessary, can change the algorithm species to maintain a quality of echo suppression that is optimal for the current computational budget. If a change of the algorithm species is initiated, then at least a portion of internal algorithm data from the previously running algorithm species might be transferred for use in the subsequent algorithm species.

Abstract: In one embodiment, a high-level compensation (HLC) module receives samples of an input signal and determines whether a magnitude of each sample, represented in a linear domain, is relatively low or relatively high by comparing the magnitude to a threshold. If a sample is less than or equal to the threshold, then it is considered to have a relatively low magnitude and the sample is not attenuated. If a sample is greater than the threshold, then it is considered to have a relatively high magnitude and the HLC module attenuates the sample according to a “soft” non-linear function. The “soft” non-linear function is characterized by at least two of the following characteristics: the non-linear function (i) increases monotonically, (ii) forms a convex upwards curve, (iii) has a first derivative at the threshold equal to one, and (iv) has a first derivative at a maximum possible magnitude value equal to zero.

Abstract: In one embodiment, an acoustic echo control (AEC) module receives an outgoing signal and an incoming signal, which, at various times, contains acoustic echo corresponding to the outgoing signal. The AEC module has a delay estimation block that estimates, in the time domain, the echo delay using an adaptive filtering technique. This delay estimation is used to align samples of the incoming signal having acoustic echo with the corresponding samples of the outgoing signal from which the acoustic echo originated. The AEC module determines whether or not samples of the incoming signal contain acoustic echo based on the aligned outgoing signal, and the determinations are applied to a hangover counter. The AEC module then suppresses acoustic echo in the incoming signal and adds comfort noise to the incoming signal. The amount of echo suppression performed is gradually increased or decreased based on comparisons of the counter to a hangover threshold.

Abstract: In one embodiment, a pause-based music detection (MD) module detects music by analyzing pauses in a received audio signal. The energy of each frame of the signal is compared to an energy threshold to determine whether the frame corresponds to background noise only (i.e., a pause) or sound such as speech or music. A window having a number of frames is analyzed to determine whether there is a pause within the window. If no pauses are detected in the window, then the current frame is presumed to correspond to music. If a pause is detected, then the current frame is presumed to correspond to speech. In another embodiment, the pause-based MD module output is applied to Boolean “OR” logic along with a tone-based MD module output to generate a final MD decision. The tone-based MD module detects music by analyzing tones in the signal using any suitable tone-based MD algorithm.

Abstract: In one embodiment, a music detection (MD) module accumulates sets of one or more frames and performs FFT processing on each set to recover a set of coefficients, each corresponding to a different frequency k. For each frame, the module identifies candidate musical tones by searching for peak values in the set of coefficients. If a coefficient corresponds to a peak, then a variable TONE[k] corresponding to the coefficient is set equal to one. Otherwise, the variable is set equal to zero. For each variable TONE[k] having a value of one, a corresponding accumulator A[k] is increased. Candidate musical tones that are short in duration are filtered out by comparing each accumulator A[k] to a minimum duration threshold. A determination is made as to whether or not music is present based on a number of candidate musical tones and a sum of candidate musical tone durations using a state machine.

Abstract: An adaptive filter configured to use multiple algorithm species that differ in the quality of echo suppression and respective burdens imposed on the computational resources of the host communication device. Depending on the available computational budget, the adaptive filter selects an algorithm species that, while supporting a relatively high quality of echo suppression, involves a relatively low risk of overwhelming the computational resources. The adaptive filter monitors changes in the available computational budget and, if appropriate or necessary, can change the algorithm species to maintain a quality of echo suppression that is optimal for the current computational budget. If a change of the algorithm species is initiated, then at least a portion of internal algorithm data from the previously running algorithm species might be transferred for use in the subsequent algorithm species.