Abstract: An apparatus for reducing noise includes a comparator for generating a noise timing signal corresponding to a noise producing period of noise introduced from a noise source and contained in an audio signal, a gap time generator for generating a gap period in which to remove noise from the audio signal, a selector switch for selectively outputting the audio signal and a noise-removed signal, a level detector for detecting a signal level of the audio signal, and a masking degree determining unit for determining from the signal level detected by the level detector a gap period for which the audio signal is masked by the human auditory system. The selector switch outputs the noise-removed signal in a period corresponding to the gap period within the noise producing period of the noise timing signal, and outputs the audio signal in other than the gap period.

Abstract: A personal listening device used in connection with the amplification of signals for enhancing the perceived quality and ambiance of sound heard by listener using stereo headphones or small speaker systems utilizes a difference signal that is cross-fed between stereo channels. The invention provides a perception of spatial presence in music sound when used with either stereo headphones or small speaker systems. The circuitry, wiring and packaging provide for multiple output jacks allowing for convenient sharing of the enhanced output signal from a personal listening device with other persons using headphones. The invention provides effects similar to stereo enhancement, surround sound, and psychoacoustic imagery in a small, inexpensive package for enhancing a user's audio experience.

Abstract: A method constructs a location model for locating and tracking sources of acoustic signals. Acoustic training signals are acquired from an acoustic training source at an unknown location in an environment with an array of microphones placed at unknown positions in the environment. From each acoustic training signal, relative acoustic features are extracted to construct a location model that is trained with the relative acoustic features.