Abstract:
Voice activity detection (VAD) is an enabling technology for a variety of speech based applications. Herein disclosed is a robust VAD algorithm that is also language independent. Rather than classifying short segments of the audio as either “speech” or “silence”, the VAD as disclosed herein employees a soft-decision mechanism. The VAD outputs a speech-presence probability, which is based on a variety of characteristics.
Abstract:
Systems, methods, and software for operating an image processing system are provided herein. In a first example, a method of operating an image processing system is provided. The method includes identifying object pixels associated with an object of interest in a scene, identifying additional pixels to associate with the object of interest, and performing an operation based on a depth of the object in the scene on target pixels comprised of the object pixels and the additional pixels to change a quality of the object of interest.
Abstract:
Systems, methods, and software for operating an image processing system are provided herein. In a first example, a method of operating an image processing system is provided. The method includes identifying object pixels associated with an object of interest in a scene, identifying additional pixels to associate with the object of interest, and performing an operation based on a depth of the object in the scene on target pixels comprised of the object pixels and the additional pixels to change a quality of the object of interest.
Abstract:
Systems and methods of automated ontology development include a corpus of communication data. The corpus of communication data includes communication data from a plurality of interactions and is processed. A plurality of terms are extracted from the corpus. Each term of the plurality is a plurality of words that identify a single concept within the corpus. An ontology is automatedly generated from the extracted terms.
Abstract:
Methods and systems for keyword spotting, i.e., for identifying textual phrases of interest in input data. In the embodiments described herein, the input data comprises communication packets exchanged in a communication network. The disclosed keyword spotting techniques can be used, for example, in applications such as Data Leakage Prevention (DLP), Intrusion Detection Systems (IDS) or Intrusion Prevention Systems (IPS), and spam e-mail detection. A keyword spotting system holds a dictionary of textual phrases for searching input data. In a communication analytics system, for example, the dictionary defines textual phrases to be located in communication packets—such as e-mail addresses or Uniform Resource Locators (URLs).
Abstract:
Methods and systems for identifying network users who communicate with the network (e.g., the Internet) via a given network connection. The disclosed techniques analyze traffic that flows in the network to determine, for example, whether the given network connection serves a single individual or multiple individuals, a single computer or multiple computers. A Profiling System (PS) acquires copies of data traffic that flow through network connections that connect computers to the WAN. The PS analyzes the acquired data, attempting to identify individuals who login to servers.
Abstract:
Methods and systems for monitoring, analyzing and acting upon voice calls in communication networks. An identification system receives monitored voice calls that are conducted in a communication network. Some of the monitored voice calls may be conducted by target individuals who are predefined as suspects. In order to maintain user privacy, the system selects and retains only voice calls that are suspected of being conducted by predefined targets. The techniques disclosed herein are particularly advantageous in scenarios where the network identifiers of the terminal used by the target are not known, or where the target uses public communication devices. In accordance with the disclosure, content-based identifiers such as speaker recognition or keyword matching are used.
Abstract:
Systems and method of diarization of audio files use an acoustic voiceprint model. A plurality of audio files are analyzed to arrive at an acoustic voiceprint model associated to an identified speaker. Metadata associate with an audio file is used to select an acoustic voiceprint model. The selected acoustic voiceprint model is applied in a diarization to identify audio data of the identified speaker.
Abstract:
A method executed by a computer system for detecting edges comprises receiving an image comprising a plurality of pixels, determining a phase congruency value for a pixel, where the phase congruency value comprises a plurality of phase congruency components, and determining if the phase congruency value satisfies a phase congruency criteria. If the phase congruency value satisfies the phase congruency criteria, the computer system categorizes the pixel as an edge pixel. If the phase congruency value does not satisfy the phase congruency criteria, the computer system compares a first phase congruency component of the plurality of phase congruency components to a phase congruency component criteria. If the first phase congruency component satisfies the phase congruency component criteria, the computer system categorizes the pixel as an edge pixel, and if the first phase congruency component does not satisfy the phase congruency component criteria, categorizes the pixel as a non-edge pixel.
Abstract:
A rule engine configured with at least one hash table which summarizes the rules managed by the engine. The rule engine receives rules and automatically adjusts the hash table in order to relate to added rules and/or in order to remove cancelled rules. The adjustment may be performed while the rule engine is filtering packets, without stopping. The rules may be grouped into a plurality of rule types and for each rule type the rule engine performs one or more accesses to at least one hash table to determine whether any of the rules of that type match the packet. In some embodiments, the rule engine may automatically select the rule types responsive to a set of rules provided to the rule engine and adapt its operation to the specific rules it is currently handling, while not spending resources on checking rule types not currently used.