A device for acoustic monitoring of a monitoring area includes first and second sensor systems which have first and second acoustic sensors, processors, and transmitter, respectively, and which may be mounted at different locations of the monitoring area. The first and second processors may be configured to classify first and second audio signals detected by the first and second acoustic sensors so as to obtain first and second classification results, respectively. The first and second transmitter may be configured to transmit the first and second classification results to a central evaluator, respectively. In addition, the device may include the central evaluator, which may be configured to receive the first classification result and to receive the second classification result, and to generate a monitoring output for the monitoring area as a function of the first classification result and the second classification result.

A device for acoustic monitoring of a monitoring area includes first and second sensor systems which have first and second acoustic sensors, processors, and transmitter, respectively, and which may be mounted at different locations of the monitoring area. The first and second processors may be configured to classify first and second audio signals detected by the first and second acoustic sensors so as to obtain first and second classification results, respectively. The first and second transmitter may be configured to transmit the first and second classification results to a central evaluator, respectively. In addition, the device may include the central evaluator, which may be configured to receive the first classification result and to receive the second classification result, and to generate a monitoring output for the monitoring area as a function of the first classification result and the second classification result.

Implementations relate to techniques for providing context-dependent search results. A computer-implemented method includes receiving an audio stream at a computing device during a time interval, the audio stream comprising user speech data and background audio, separating the audio stream into a first substream that includes the user speech data and a second substream that includes the background audio, identifying concepts related to the background audio, generating a set of terms related to the identified concepts, influencing a speech recognizer based on at least one of the terms related to the background audio, and obtaining a recognized version of the user speech data using the speech recognizer.

Implementations relate to techniques for providing context-dependent search results. A computer-implemented method includes receiving an audio stream at a computing device during a time interval, the audio stream comprising user speech data and background audio, separating the audio stream into a first substream that includes the user speech data and a second substream that includes the background audio, identifying concepts related to the background audio, generating a set of terms related to the identified concepts, influencing a speech recognizer based on at least one of the terms related to the background audio, and obtaining a recognized version of the user speech data using the speech recognizer.

A method and a system for speech enhancement including a time synchronization unit configured to synchronize microphone signals sent from at least two microphones; a source separation unit configured to separate the synchronized microphone signals and output a separated speech signal, which corresponds to a speech source; and a noise reduction unit including a feature extraction unit configured to extract a speech feature of the separated speech signal and a neural network configured to receive the speech feature and output a clean speech feature.

A method and a system for speech enhancement including a time synchronization unit configured to synchronize microphone signals sent from at least two microphones; a source separation unit configured to separate the synchronized microphone signals and output a separated speech signal, which corresponds to a speech source; and a noise reduction unit including a feature extraction unit configured to extract a speech feature of the separated speech signal and a neural network configured to receive the speech feature and output a clean speech feature.

The present invention provides a method for speech separation and recognition. The present invention overcomes the disadvantages of the existing techniques by providing automatic speech recognition and separation that helps managers see what their service agents and customers are saying. From there, quickly and objectively knowing the wishes and concerns of customers as well as whether their service agents can give accurate and correct advice. In addition, the system is constantly updated based on the semi-supervised training mechanism, which means that the system can self-learn from actual data during operation, thereby helping to improve the system's accuracy.

The present invention provides a method for speech separation and recognition. The present invention overcomes the disadvantages of the existing techniques by providing automatic speech recognition and separation that helps managers see what their service agents and customers are saying. From there, quickly and objectively knowing the wishes and concerns of customers as well as whether their service agents can give accurate and correct advice. In addition, the system is constantly updated based on the semi-supervised training mechanism, which means that the system can self-learn from actual data during operation, thereby helping to improve the system's accuracy.

Disclosed are systems and methods for a frontend capture module of a video conferencing application, which can modify an input signal, received from a microphone device to match predetermined signal characteristics, such as voice signal level and expected noise floor. An Input stage, a suppression module and an output stage amplify the voice signal portion of the input signal and suppress the noise signal of input signal to predetermined ranges. The input stage selectively applies gains defined by a gain table, based on signal level of the input signal. The suppression module selectively applies a suppression gain to the input signal based on presence or absence of voice signal in the input signal. The output stage further amplifies the input signal in portions having a voice signal and applies a gain table to maintain a consistent noise floor.

Disclosed are systems and methods for a frontend capture module of a video conferencing application, which can modify an input signal, received from a microphone device to match predetermined signal characteristics, such as voice signal level and expected noise floor. An Input stage, a suppression module and an output stage amplify the voice signal portion of the input signal and suppress the noise signal of input signal to predetermined ranges. The input stage selectively applies gains defined by a gain table, based on signal level of the input signal. The suppression module selectively applies a suppression gain to the input signal based on presence or absence of voice signal in the input signal. The output stage further amplifies the input signal in portions having a voice signal and applies a gain table to maintain a consistent noise floor.