Abstract: An echo cancellation technique that can process multi-input microphone signals with only a small increase in the overall CPU consumption compared to implementing the algorithm for a single channel microphone signal. Furthermore, the invention provides an architecture that provides for echo cancellation for multiple applications in parallel with only a small increase in CPU consumption compared to a single instance of echo cancellation with a single microphone input and multi-output channel playback.

Abstract: A regression-based residual echo suppression (RES) system and process for suppressing the portion of the microphone signal corresponding to a playback of a speaker audio signal that was not suppressed by an acoustic echo canceller (AEC). In general, a prescribed regression technique is used between a prescribed spectral attribute of multiple past and present, fixed-length, periods (e.g., frames) of the speaker signal and the same spectral attribute of a current period (e.g., frame) of the echo residual in the output of the AEC. This automatically takes into consideration the correlation between the time periods of the speaker signal. The parameters of the regression can be easily tracked using adaptive methods. Multiple applications of RES can be used to produce better results and this system and process can be applied to stereo-RES as well.

Abstract: Center clipping is applied with acoustic echo suppression in a two-way voice communication system to reduce a microphone signal to the background noise floor when speech is not present. For integration with a microphone array, the center clipping processing determines whether speech is present based on estimates of the overall leak through and instantaneous microphone power across the microphone array channels. The overall estimates can be calculated as a dot product of the microphone array coefficients computed by a sound source localization process and separate estimates for the respective microphone channel.

Abstract: An approach for identifying suspect network sites in a network environment entails using one or more malware analysis modules to identify distribution sites that host malicious content and/or benign content. The approach then uses a linking analysis module to identify landing sites that are linked to the distribution sites. These linked sites are identified as suspect sites for further analysis. This analysis can be characterized as “bottom up” because it is initiated by the detection of potentially problematic distribution sites. The approach can also perform linking analysis to identify a suspect network site based on a number of alternating paths between that network site and a set of distribution sites that are known to host malicious content. The approach can also train a classifier module to predict whether an unknown landing site is a malicious landing site or a benign landing site.

Abstract: Acoustic echo cancellation, residual echo suppression and sound-source localization/microphone array processes are combined in a two-way voice communication system that uses a microphone array to capture local speech. The processes can be configured according to various alternative architectures and enhancements made to the processes to avoid the adverse effects of non-linear operations in the residual echo suppression on the sound-source localization/microphone array process.

Abstract: A method of identifying a malware file using multiple classifiers is disclosed. The method includes receiving a file at a client computer. The file includes static metadata. A set of metadata classifier weights are applied to the static metadata to generate a first classifier output. A dynamic classifier is initiated to evaluate the file and to generate a second classifier output. The method includes automatically identifying the file as potential malware based on at least the first classifier output and the second classifier output.

Abstract: A method for classifying search query traffic can involve receiving a plurality of labeled sample search query traffic and generating a feature set partitioned into human physical limit features and query stream behavioral features. A model can be generated using the plurality of labeled sample search query traffic and the feature set. Search query traffic can be received and the model can be utilized to classify the received search query traffic as generated by a human or automatically generated.

Abstract: Harmonic distortion residual echo suppression (HDRES) technique embodiments are presented which act to suppress the residual echo remaining after a near-end microphone signal has undergone AEC, including harmonic distortion in the signal that was caused by the speaker audio signal playback. In general, an AEC module is employed which suppresses some parts of the speaker audio signal found in a near-end microphone signal and generates an AEC output signal. A HDRES module then inputs the AEC output signal and the speaker audio signal, and suppresses at least a portion of a residual part of the speaker audio signal that was left unsuppressed by the AEC module. This includes at least a portion of the harmonic distortion exhibited in the AEC output signal.

Abstract: A malicious behavior detection/prevention system, such as an intrusion detection system, is provided that uses active learning to classify entries into multiple classes. A single entry can correspond to either the occurrence of one or more events or the non-occurrence of one or more events. During a training phase, entries are automatically classified into one of multiple classes. After classifying the entry, a generated model for the determined class is utilized to determine how well an entry corresponds to the model. Ambiguous classifications along with entries that do not fit the model well for the determined class are selected for labeling by a human analyst The selected entries are presented to a human analyst for labeling. These labels are used to further train the classifier and the models. During an evaluation phase, entries are automatically classified using the trained classifier and a policy associated with determined class is applied.

Abstract: Hybrid echo canceller controllers are described herein. By way of example, a system for controlling an echo canceller can include a signal indicator and an echo canceller controller. The signal indicator can be configured to indicate periods of near-end signal and to indicate periods of echo only with echo-path change in the corrupted signal based at least in part on cross-correlation between two signals associated with the echo canceller. The echo canceller controller can be configured to control the echo canceller according to indications from the signal indicator.

Abstract: Cross-correlation based echo canceller controllers are described herein. By way of example, a system for controlling an echo canceller having one or more adaptive filters can include one or more adaptive filter controllers each corresponding to one of the one or more adaptive filters and each configured to halt adaptation of its corresponding adaptive filter according to the cross-correlation of its corresponding corrupted signal and its corresponding error signal of its corresponding adaptive filter.

Abstract: Hybrid echo canceller controllers are described herein. By way of example, a system for controlling an echo canceller can include a cross-correlator, a discriminator and an echo canceller controller. The cross-correlator can be configured to produce a cross-correlation based output that facilitates controlling the echo canceller by cross-correlating two signals associated with the echo canceller. The discriminator can be configured to produce a discriminator output that discriminates between near-end signal and echo in a corrupted signal. The echo canceller controller can be configured to control the echo canceller according to the cross-correlation based output and the discriminator output.

Abstract: Signal detectors are described herein. By way of example, a system for detecting signals can include a microphone signal detector, a loudspeaker signal detector, a signal discriminator and a decision component. When the microphone signal detector detects the presence of a microphone signal, the loudspeaker signal detector detects the presence of a loudspeaker signal and the signal discriminator determines that near-end speech dominates loudspeaker echo, the decision component can confirm the presence of doubletalk. When the microphone signal detector detects the presence of a microphone signal and the signal discriminator determines that near-end speech dominates loudspeaker echo, the decision component confirms the presence of near-end signal.

Abstract: A method and system of multi-channel echo cancellation using round robin regularization. The multi-channel round robin regularization echo cancellation method includes applying a plurality of adaptive filters, each having an inverse correlation matrix, to the multi-channel playback signal. Each of the plurality of adaptive filters is selected in a round robin sequence, so that every round each of the filters is selected. The inverse correlation matrix associated with each selected adaptive filter then is regularized as needed. The regularized adaptive filter then is used to remove the echo of the multi-channel playback signal from a captured signal. Regularization is implemented in a round robin manner to ensure that each subband is selected so that the adaptive filter for that subband can be examined. Other features of the multi-channel echo cancellation system and method include dynamic switching between monaural and multi-channel echo cancellation and mixed processing for lower and upper subbands.

Abstract: An acoustic echo cancellation technique. The present adaptive acoustic echo cancellation technique employs a plurality of acoustic echo cancellation filters which use different adaptation techniques which may employ different parameters such as step size, to improve both the adaptation algorithm convergence time and misadjustment over previously known acoustic echo cancellation techniques.

Abstract: The present invention relates to systems and methods that remove echo from a signal via a novel echo cancellation technique that supports arbitrary playback sampling rates. The novel echo cancellation technique transforms a playback signal to a frequency domain representation and converts its sampling rate to a sampling rate of a frequency domain transformed received signal for the appropriate number of frequency bins. This conversion is achieved via an exact or interpolated approached. The re-sampled playback signal transform is then utilized in connection with the received signal transform to remove echo associated with the playback signal from the received signal.

Abstract: An audio processing system is used to process digital audio signals that represent sound emanating from a source that is moving through three dimensional space. The audio processing system has a filter unit that employs infinite impulse response (IIR) filters to filter the audio signals. The IIR filters have filter coefficients that change when the sound source is stationary or moves from one location to the next in the 3D space. To minimize the transient response following a coefficient change, the filter unit initializes elements in the tap delay lines of the IIR filters to non-zero values. In one implementation, the tap delay line elements are changed to a set of predetermined non-zero values. In another implementation, the tap delay line elements are initialized to the final values produced by the filter for the previous sound source location.