Automatic dynamic VOX circuit

A prosthetic hearing implant capable of independently and concurrently processing multiple audio input signals such that each audio input signal is separately utilized to stimulate the basilar membrane of the implant recipient. A prosthetic hearing implant of the present invention simultaneously receives discrete audio signals from each of one or more audio sources, generates a separate set of one or more stimulations signals for each audio signal, and concurrently applies the stimulation signals to the cochlear. Different channels of stimulation may be allocated to each audio input signal. Alternatively, different modes of stimulation may be used for each input signal. Or, in yet another example, the audio input signals may be time-multiplexed such that the input signals are applied in alternating timeslots. Or, in yet other examples, the input signals may be applied using a combination of stimulation channels, modes of stimulation, and alternating time slots. The stimulation signals may be delivered to the same or different electrodes depending on the quantity and frequency component of the received audio input signals, the quantity of available stimulation channels, and other factors.

A system and method of audio processing provides enhanced speech recognition. Audio input is received at a plurality of microphones. The multi-channel audio signal from the microphones may be processed by a beamforming network to generate a single-channel enhanced audio signal, on which voice activity is detected. Audio signals from the microphones are additionally processed by an adaptable noise cancellation filter having variable filter coefficients to generate a noise-suppressed audio signal. The variable filter coefficients are updated during periods of voice inactivity. A speech recognition engine may apply a speech recognition algorithm to the noise-suppressed audio signal and generate an appropriate output. The operation of the speech recognition engine and the adaptable noise cancellation filter may advantageously be controlled based on voice activity detected in the single-channel enhanced audio signal from the beamforming network.

A sound processing method and apparatus are provided, which are capable of performing sound processing on input audio signals containing a plurality of signal components being different in desired sound processing conditions, in a manner that allows natural sound to be reproduced. An input audio signal of at least one system is separated into a plurality of separated signal components, and each signal component of at least part of the plurality of separated signal components is subjected to individual sound processing according to the signal component, and the plurality of separated signal components are outputted as at least one audio signal after each signal component of the at least part thereof is subjected to the individual sound processing. The plurality of separated signal components are synthesized into a synthesized audio signal, which is then outputted, or alternatively, the plurality of separated signal components are outputted separately as audio signals.

A bone conduction speaker and mounting system allows a person to perceive audible sounds, such as music, through his sense of touch. A bone conduction speaker or transducer is mounted against a person's body, preferably against the sternum. An audio signal is fed to the bone conduction speaker or transducer which converts the audio signal to vibrations. The bone conduction speaker or transducer transmits the vibrations to the person's rib-cage which will then resonate in synchronism with the input audio signal. The audio signal may also be simultaneously fed to regular audio speakers to allow a person to hear as well as feel the audible sounds or music.

This paper presents an ultra-low-power voice activity detection (VAD) system to discriminate speech from non-speech parts of audio signals. The proposed VAD system uses level-crossing sampling for voice activity detection. The useless samples in the non-speech parts of the signal are eliminated due to the activity-dependent nature of this sampling scheme. A 40 ms moving window with a 30 ms overlap is exploited as a feature extraction block, within which the output samples of the level-crossing analog-to-digital converter (LC-ADC) are counted as the feature. The only variable used to distinguish speech and non-speech segments in the audio input signal is the number of LC-ADC output samples within a time window. The proposed system achieves an average of 91.02% speech hit rate and 82.64% non-speech hit rate over 12 noise types at −5, 0, 5, and 10 dB signal-to-noise ratios (SNR) over the TIMIT database. The proposed system including LC-ADC, feature extraction, and classification circuits was designed in 0.18 µm CMOS technology. Post-layout simulation results show a power consumption of 394.6 nW with a silicon area of 0.044 mm2, which makes it suitable as an always-on device in an automatic speech recognition system.

A method of altering a social signaling characteristic of a speech signal. A statistically large number of speech samples created by different speakers in different tones of voice are evaluated to determine one or more relationships that exist between a selected social signaling characteristic and one or more measurable parameters of the speech samples. An input audio voice signal is then processed in accordance with these relationships to modify one or more of controllable parameters of input audio voice signal to produce a modified output audio voice signal in which said selected social signaling characteristic is modified. In a specific illustrative embodiment, a two-level hidden Markov model is used to identify voiced and unvoiced speech segments and selected controllable characteristics of these speech segments are modified to alter the desired social signaling characteristic.

Variations in the amplitude-frequency response characteristic of a loudspeaker are compensated by a drive circuit in which an audio input signal is summed with a plurality of delayed replicas of the input signal to provide a drive signal for the loudspeaker. The delayed replicas are derived by discrete time filtering. Noise reduction techniques applied to the least delayed of the plurality of delay signals substantially improves the S/N ratio in the drive signal.

A waveform-based technique for generating periodicity information from an input signal includes generating a pre-processed signal by applying low pass and non-linear filtering to the input signal, wherein the pre-processed signal has highlighted speech pitch tracks. An adaptive threshold algorithm is applied to the pre-processed signal to generate a detection signal having waveform segments whose peaks are separated by a pitch period of the input signal. A period between peaks in the detection signal is determined that indicates the periodicity information. Information about the period between the peaks in the detection signal is then used to adapt a scaling value to be used by the adaptive threshold algorithm in a subsequent step. The periodicity information may be utilized in a voice activity detector in a telephonic communications system.

A large number of hearing impaired people in rural sector of Pakistan cannot afford the high prices of digital hearing aids. Moreover, since the analogue devices amplify the speech and noise signals equally, they are not much flexible and have functional limitations. These problems can be solved with digital devices which are generally costly. In this research work, a microcontroller based cost-effective and portable hearing aid is designed and tested. The main components include sound detection circuit, microcontroller and a digital to analogue (DAC) convertor. Device is aimed to process human voice frequencies with an adjustable gain of up to 80 dB. It is tested with an input audio signal and it showed satisfactory results for low frequency signals up to 3.5 kHz which caters for the frequency range of most human voices. The system is now being developed for more effective noise suppression and broader frequency range.

Image style transfer networks are used to blend images, producing images that are a mix of source images. The process is based on controlled extraction of style and content aspects of images, using pre-trained Convolutional Neural Networks (CNNs). Our interest lies in adopting these image style transfer networks for the purpose of transforming sounds. Audio signals can be presented as grey-scale images of audio spectrograms. The purpose of our work is to investigate whether audio spectrogram inputs can be used with image neural transfer networks to produce new sounds. Using musical instrument sounds as source sounds, we apply and compare three existing image neural style transfer networks for the task of sound mixing. Our evaluation shows that all three networks are successful in producing consistent, new sounds based on the two source sounds. We use classification models to demonstrate that the new audio signals are consistent and distinguishable from the source instrument sounds. We further apply t-SNE cluster visualisation to visualise the feature maps of the new sounds and original source sounds, confirming that they form different sound groups from the source sounds. Our work paves the way to using CNNs for creative and targeted production of new sounds from source sounds, with specified source qualities, including pitch and timbre.

A two‐channel single‐stage switching amplifier (SSA) based on high‐frequency link inverter is proposed. The audio input signals are modulated by phase shift between primary‐side switches and secondary‐side switches, thus independent amplifications of two channels can be obtained. Benefiting from multiplexing of primary‐side power switches for two channels, only a half bridge inverter is utilised at primary side. Clamp circuits at the secondary side of transformer eliminate spike voltages across the secondary‐side switches during the current commutation of the cycloconverters. Zero voltage switching of primary‐side switches and clamp switches are achieved by utilising magnetising inductor current. A 320 V/250W prototype is presented to verify the analysis results of the proposed two‐channel SSA.

Similar to Haptuator from Tactile Labs Inc. and Force Reactor from Alps Electric Co., DC motor can produce high fidelity vibration when it is driven by alternative current such as audio input signal. In our study, we developed a new vibration actuator using a DC motor that can generate much stronger vibrations than a normal DC motor and produce very low frequency of vibrations. We proposed that the stator of the motor could be used as the vibration mass by fixing the rotor of the actuator. To evaluate this design concept, we developed a prototype actuator that can be driven in two modes: stator mode (i.e., the new design concept) and normal mode. The experiment results revealed that stronger vibrations can be obtained on a fingertip in stator mode because the fixed part that comprises the rotor was lighter and the vibration mass using the stator was heavier. We also confirmed that the actuator can be driven at very low frequency (1 Hz) in stator mode.

This paper deals with implementing a real-time gunshot detection algorithm on the digital signal processor TMS320C6713. The developed algorithm uses 3 linear predictive coding coefficients, energy in 3 spectral bands, and frame correlation. The audio input signal is continuously processed and signal frames considered to contain a gunshot are shortly signalized by an LED indicator. Experimental results achieved 82% correctly detected gunshots and 3% false alarms. The system should increase protection of wild elephants in Africa.

Many signal processing-based methods for sound source direction-of-arrival estimation produce a spatial pseudo-spectrum of which the local maxima strongly indicate the source directions. Due to different levels of noise, reverberation and different number of overlapping sources, the spatial pseudo-spectra are noisy even after smoothing. In addition, the number of sources is often unknown. As a result, selecting the peaks from these spectra is susceptible to error. Convolutional neural network has been successfully applied to many image processing problems in general and direction-of-arrival estimation in particular. In addition, deep learning-based methods for direction-of-arrival estimation show good generalization to different environments. We propose to use a 2D convolutional neural network with multi-task learning to robustly estimate the number of sources and the directions-of-arrival from short-time spatial pseudo-spectra, which have useful directional information from audio input signals. This approach reduces the tendency of the neural network to learn unwanted association between sound classes and directional information, and helps the network generalize to unseen sound classes. The simulation and experimental results show that the proposed methods outperform other directional-of-arrival estimation methods in different levels of noise and reverberation, and different number of sources.

A system with an accompanying method is provided to improve the signal-to-noise ratio (SNR) of noisy speech by suppressing acoustic background noise. The background noise consists of narrow band noise from rotating machine, audio signals from stereo-loudspeakers of audio entertainment device, and other ambient noise. In this system/apparatus, a microphone senses the speech intermingled with the background noise, and another microphone senses the noisy background. In addition, a measurement sensor is used to measure RPM (revolutions-per-minutes) of the rotating machine and two wires are used to acquire audio signals from the stereo-loudspeakers of the audio entertainment device. Furthermore, to provide better suppression performance for the acoustic audio signals, the characteristics of these loudspeakers are used to compensate for the distortion caused by the loudspeakers. Adaptive comb filters and adaptive FIR filters are applied to estimate the ambient noise and suppress the background noise. After processing, the system outputs the enhanced speech signal with higher SNR.