An electrical device for reducing noise, comprises a first microphone configured to receive soundwave from a sound source and convert the soundwave to a first electrical signal including a noise component, a second microphone configured to receive ambient noise from an ambient environment and convert the ambient noise to a second electrical signal. The second electrical signal is reversed in polarity to the first electrical signal. The electrical device further comprises a circuit connecting the first microphone and the second microphone. The circuit is configured to combine the first electrical signal and the second electrical signal in order to reduce the noise component in the first electrical signal with the second electrical signal that is reversed in polarity.

This invention provides a new and improved voice communication system with high quality noise cancellation method and devices to overcome the limitations and difficulties encountered in conventional technologies. This invention discloses a noise cancellation apparatus that includes a vibration sensor and a microphone for receiving and transmitting voice signals as incoming speeches. The vibration sensor is applied to receive vibration signals corresponding to the voice signals for applying the vibration signals as reference signals for removing noise signals generated from environmental noises by converting vibration signals to intermediate PDL representation together with the speaker characteristics, mapping them into full band high quality clean acoustic representation, and synthesizing clear personal speech with characteristics identical to the original microphone speech without noises.

A home appliance including a first microphone that is disposed on a surface of a housing, a second microphone that is disposed on an inside of the housing, and a processor configured to perform signal processing for first voice data that is acquired from the first microphone, and perform voice recognition using the signal-processed first voice data. The processor is further configured to generate noise data using second voice data that is acquired from the second microphone and perform signal processing for the first voice data using the generated noise data.

A telemedicine system comprises (i) medical devices that are each configured to generate patient datasets, and (ii) a remote web server. At least one of the medical devices is then configured to upload or send patient datasets to the remote web server, directly from an internet-connected app running either on the medical device or on at least one intermediary device. The remote web server is configured to generate a unique web-link that is associated with a specific patient dataset. The unique web-link enables a healthcare professional to review the specific patient dataset by selecting the web-link from within a web browser or from within any dedicated telemedicine application that opens web-links.

Methods, systems, and computer-readable media are provided for detecting voice activity. A primary signal is configured to include a speech component representative of a user's speech when the user is speaking in a detection region, or environment. A reference signal is configured to include a reduced speech component relative to the primary signal. One or more conditions of the detection region is/are detected, and a threshold value is selected (or, optionally, calculated) based upon the detected condition(s). The primary signal is compared to the reference signal, with respect to the selected threshold value. An indication of whether the user is speaking is selectively output, based at least in part upon the comparison.

By performing both noise cancellation and echo cancellation, a high-quality main voice signal is generated. A voice input/output apparatus includes a noise acquirer that is arranged toward an outside of a body of a user and acquires external noise arriving from the outside of the user, a voice output unit that accepts an input of a voice signal and outputs a voice to an ear canal of the user, a main voice acquirer that acquires a mixed voice, in which the external noise, the output voice, and a main voice of the user transmitted from a vocal cord of the user through the ear canal are mixed, and outputs a mixed voice signal, a noise canceler that processes the mixed voice signal using a noise signal based on the external noise, and an echo canceler that processes the mixed voice signal using the voice signal.

The present invention discloses a dual-microphone adaptive filtering algorithm for collecting body sound signals, characterized in that, using at least two microphones, a primary microphone and a secondary microphone, to collect signals; the primary microphone is used to collect noisy body sound signals, and the secondary microphone is used to collect environmental noise; applying a same high-pass filtering to signals collected by the primary microphone and signals collected by the secondary microphone; using a normalized least mean square algorithm on the primary microphone signals and the secondary microphone signals after the high-pass filtering to calculate a weight of the adaptive filter and to calculate an error signal to filter out environmental noise in the primary microphone signals; processing the error signal for a first time by a low-pass filtering to restore the body sound signals, to obtain the body sound signals output by the adaptive filtering algorithm. This algorithm not only may achieve rapid convergence of filter weights, but also avoid signal distortion, and suppress environmental noise interference quickly and reliably.

Various implementations include systems for processing inner microphone audio signals. In particular implementations, a system includes an external microphone configured to be acoustically coupled to an environment outside an ear canal of a user; an inner microphone configured to be acoustically coupled to an environment inside the ear canal of the user; and an adaptive noise cancelation system configured to process an internal signal captured by the inner microphone and generate a noise reduced internal signal, wherein the noise reduced internal signal is adaptively generated in response to an external signal captured by the external microphone.

A noise estimation parameter learning device is provided according to which even in a large space causing a problem of the reverberation and the time frame difference, multiple microphones disposed at distant positions cooperate with each other, and a spectral subtraction method is executed, thereby allowing the target sound to be enhanced. A noise estimation parameter learning device for learning noise estimation parameters used to estimate noise included in observed signals through a plurality of microphones, the noise estimation parameter learning device comprising: a modeling part that models a probability distribution of observed signals of the predetermined microphone, models a probability distribution of time frame differences, and models a probability distribution of transfer function gains; a likelihood function setting part that sets a likelihood function pertaining to the time frame difference, and a likelihood function pertaining to the transfer function gain, based on the modeled probability distributions; and a parameter update part that alternately and repetitively updates two variables of two likelihood functions, and outputs the time frame difference and the transfer function gain that have converged, as the noise estimation parameters.

A dual microphone signal processing arrangement for reducing reverberation is described. Time domain microphone signals are developed from a pair of sensing microphones. These are converted to the time-frequency domain to produce complex value spectra signals. A binary gain function applies frequency-specific energy ratios between the spectra signals to produce transformed spectra signals. A sigmoid gain function based on an inter-microphone coherence value between the transformed spectra signals is applied to the transformed spectra signals to produce coherence adapted spectra signals. And an inverse time-frequency transformation is applied to the coherence adjusted spectra signals to produce time-domain reverberation-compensated microphone signals with reduced reverberation components.