Abstract:
H:\kmh\Interwoven\NRPortbl\DCC\KMH\9078889 Ldocx-11/12/2015 A light-based skin contact detector is described, including a boot having an index of refraction less than or equal to another index of refraction associated with skin at a frequency of light, a light emitter and detector coupled to the boot and configured to measure an amount of light energy reflected by an interface of the boot, and a digital signal processor configured to detect a change in the amount of light energy reflected by the interface. Embodiments relate to methods for detecting skin contact by measuring an amount of energy reflected by an interface when a boot is not in contact with skin, measuring another amount of energy reflected by another interface when the boot is in contact with the skin, and detecting a change between the amount of energy and the another amount of energy using a digital signal processor. Fig. 3
Abstract:
A light-based skin contact detector is described, including a boot having an index of refraction less than or equal to another index of refraction associated with skin at a frequency of light, a light emitter and detector coupled to the boot and configured to measure an amount of light energy reflected by an interface of the boot, and a digital signal processor configured to detect a change in the amount of light energy reflected by the interface. Embodiments relate to methods for detecting skin contact by measuring an amount of energy reflected by an interface when a boot is not in contact with skin, measuring another amount of energy reflected by another interface when the boot is in contact with the skin, and detecting a change between the amount of energy and the another amount of energy using a digital signal processor.
Abstract:
A voice activity detector (VAD) combines the use of an acoustic VAD and a vibration sensor VAD as appropriate to the conditions a host device is operated. The VAD includes a first detector receiving a first signal and a second detector receiving a second signal. The VAD includes a first VAD component coupled to the first and second detectors. The first VAD component determines that the first signal corresponds to voiced speech when energy resulting from at least one operation on the first signal exceeds a first threshold. The VAD includes a second VAD component coupled to the second detector. The second VAD component determines that the second signal corresponds to voiced speech when a ratio of a second parameter corresponding to the second signal and a first parameter corresponding to the first signal exceeds a second threshold.
Abstract:
A method and system for removing acoustic noise removal (Fig. 5) from human speech is described. Acoustic noise is removed regardless of noise type, amplitude, or orientation. The system includes a processor (30) coupled among microphones (1, 2) and a voice activation detection ("V AD") element (104). The processor executes denoising algorithms that generate transfer functions. The processor (30) receives acoustic data from the microphones (1, 2) and data from the VAD (104) indicates voicing activity and when the VAD indicates no voicing activity. The transfer functions are used to generate a denoised data stream.
Abstract:
Voice Activity Detection (VAD) devices, systems and methods are described fo r use with signal processing systems to denoise acoustic signals. Components o f a signal processing system and/or VAD system receive acoustic signals and voice activity signals. Control signals are automatically generated from dat a of the voice activity signals. Components of the signal processing system and/or VAD system use the control signals to automatically select a denoisin g method appropriate to data of frequency subbands of the acoustic signals. Th e selected denoising method is applied to the acoustic signals to generate denoised acoustic signals.
Abstract:
Communication systems are described, including both portable handset and headset devices, which use a number of microphone configurations to receive acoustic signals of an environment. The microphone configurations include, f or example, a two-microphone array including two unidirectional microphones, an d a two-microphone array including one unidirectional microphone and one omnidirectional microphone. The communication systems also include Voice Activity Detection (VAD) devices to provide information of human voicing activity. Components of the communications systems receive the acoustic signals and voice activity signals and, in response, automatically generate control signals from data of the voice activity signals. Components of the communication systems use the control signals to automatically select a denoising method appropriate to data of frequency subbands of the acoustic signals. The selected denoising method is applied to the acoustic signals to generate denoised acoustic signals when the acoustic signal includes speech (101) and noise (102).
Abstract:
Systems and methods are provided for detecting voiced and unvoiced speech in acoustic signals having varying levels of background noise. The systems (Fig . 3) receive acoustic signals at two microphones (Mic 1, Mic 2), and generate difference parameters between the acoustic signals received at each of the t wo microphones (Mic 1, Mic 2). The difference parameters are representative of the relative difference in signal gain between portions of the receive acoustic signals. The systems identify information of the acoustic signals a s unvoiced speech when the difference parameters exceed a first threshold, and identify information of the acoustic signals as voiced speech when the difference parameters exceed a second threshold. Further, embodiments of the systems include non-acoustic sensors (20) that receive physiological information to aid identifying voiced speech.
Abstract:
Voice Activity Detection (VAD) devices, systems and methods are described for use with signal processing systems to denoise acoustic signals. Components of a signal processing system and/or VAD system receive acoustic signals and voice activity signals. Control signals are automatically generated from data of the voice activity signals. Components of the signal processing system and/or VAD system use the control signals to automatically select a denoising method appropriate to data of frequency subbands of the acoustic signals. The selected denoising method is applied to the acoustic signals to generate denoised acoustic signals.
Abstract:
A microphone array is described for use in ultra-high acoustical noise environments. The microphone array includes two directional close-talk microphones. The two microphones are separated by a short distance so that one microphone picks up more speech than the other. The microphone array can be used along with an adaptive noise removal program to remove a significant portion of noise from a speech signal of interest