Systems, methods, and other embodiments associated with computer distress-call detection and authentication are described. In one embodiment, a method includes detecting a human voice in audio content of a radio signal. Speech is recognized in the human voice to transform the human voice into text and vocal metrics. Feature scores are generated that represent features of the recognized speech based at least in part on the vocal metrics. The human voice is then classified as either a hoax distress call or an authentic distress call based on the feature scores. An alert is then presented indicating that the human voice is one of the hoax distress call or the authentic distress call.

Systems, methods, and other embodiments associated with computer distress-call detection and authentication are described. In one embodiment, a method includes detecting a human voice in audio content of a radio signal. Speech is recognized in the human voice to transform the human voice into text and vocal metrics. Feature scores are generated that represent features of the recognized speech based at least in part on the vocal metrics. The human voice is then classified as either a hoax distress call or an authentic distress call based on the feature scores. An alert is then presented indicating that the human voice is one of the hoax distress call or the authentic distress call.

In order to detect a replay attack in a speaker recognition system, at least one feature is identified in a detected magnetic field. It is then determined whether the at least one identified feature of the detected magnetic field is indicative of playback of speech through a loudspeaker. If so, it is determined that a replay attack may have taken place.

In order to detect a replay attack in a speaker recognition system, at least one feature is identified in a detected magnetic field. It is then determined whether the at least one identified feature of the detected magnetic field is indicative of playback of speech through a loudspeaker. If so, it is determined that a replay attack may have taken place.

Detecting a replay attack on a voice biometrics system comprises: receiving a speech signal from a voice source; generating and transmitting an ultrasound signal through a transducer of the device; detecting a reflection of the transmitted ultrasound signal; detecting Doppler shifts in the reflection of the generated ultrasound signal; and identifying whether the received speech signal is indicative of liveness of a speaker based on the detected Doppler shifts. The method further comprises: obtaining information about a position of the device; and adapting the generating and transmitting of the ultrasound signal based on the information about the position of the device.

Detecting a replay attack on a voice biometrics system comprises: receiving a speech signal from a voice source; generating and transmitting an ultrasound signal through a transducer of the device; detecting a reflection of the transmitted ultrasound signal; detecting Doppler shifts in the reflection of the generated ultrasound signal; and identifying whether the received speech signal is indicative of liveness of a speaker based on the detected Doppler shifts. The method further comprises: obtaining information about a position of the device; and adapting the generating and transmitting of the ultrasound signal based on the information about the position of the device.

Background noise estimators and methods are disclosed for estimating background noise in an audio signal. Some methods include obtaining at least one parameter associated with an audio signal segment, such as a frame or part of a frame, based on a first linear prediction gain, calculated as a quotient between a residual signal from a 0th-order linear prediction and a residual signal from a 2nd-order linear prediction for the audio signal segment. A second linear prediction gain is calculated as a quotient between a residual signal from a 2nd-order linear prediction and a residual signal from a 16th-order linear prediction for the audio signal segment. Whether the audio signal segment comprises a pause is determined based at least on the obtained at least one parameter; and a background noise estimate is updated based on the audio signal segment when the audio signal segment comprises a pause.

An electronic device is provided. The electronic device includes, for the purpose of determining a customized beamformer filter, an input module including a plurality of microphones configured to receive an external sound signal, a memory configured to store computer-executable instructions and an initial value of a voice parameter used to perform beamforming on the external sound signal, and a processor configured to execute the instructions by accessing the memory. The instructions may be configured to estimate a feature value of the external sound signal, calculate the initial value of the voice parameter used to perform beamforming based on the external sound signal received by the plurality of microphones, determine whether to store the calculated initial value according to the feature value, determine which one of the calculated initial value or an initial value stored in the memory used according to the feature value, and obtain a target voice parameter.

The invention provides an audio encoder for encoding an audio signal so as to produce therefrom an encoded signal, the audio encoder including: a framing device configured to extract frames from the audio signal; a quantizer configured to map spectral lines of a spectrum signal derived from the frame of the audio signal to quantization indices, wherein the quantizer has a dead-zone, in which the input spectral lines are mapped to quantization index zero; and a control device configured to modify the dead-zone; wherein the control device includes a tonality calculating device configured to calculate at least one tonality indicating value for at least one spectrum line or for at least one group of spectral lines, wherein the control device is configured to modify the dead-zone for the at least one spectrum line or the at least one group of spectrum lines depending on the respective tonality indicating value.

A microphone may comprise a microphone element for detecting sound, and a digital signal processor configured to process a first audio signal that is based on the sound in accordance with a selected one of a plurality of digital signal processing (DSP) modes. Each of the DSP modes may be for processing the first audio signal in a different way. For example, the DSP modes may account for distance of the person speaking (e.g., near versus far) and/or desired tone (e.g., darker, neutral, or bright tone). At least some of the modes may have, for example, an automatic level control setting to provide a more consistent volume as the user changes their distance from the microphone or changes their speaking level, and that may be associated with particular default (and/or adjustable) values of the parameters attack, hold, decay, maximum gain, and/or target gain, each depending upon which DSP is being applied.