Showing papers on "Digital hearing aid published in 2020"
TL;DR: A low complexity design of a digital finite impulse response (FIR) filter for digital hearing aid application and the results shows that the proposed architecture has less slices than best existing designs.
Abstract: Hearing aid is an acoustic device which is worn by hearing loss people. To compensate the different types of hearing loss, it is necessary to selectively amplify sounds at required frequencies. The main aim of the hearing aid is to selectively remove the noise signal such that the processed sound matches ones audiogram. To achieve this, the decimation filter in hearing aids can be design using multiplier less architecture which should be able to adjust sound levels at arbitrary frequencies within a given spectrum. In hearing aids, decimation filter plays a key role. This paper presents a low complexity design of a digital finite impulse response (FIR) filter for digital hearing aid application. This paper proposed approximate 4:2 compressor adders in memory less DA based FIR filter architecture. In DA architecture the area of the ROM increases gradually when filter order is increased. Memory less DA is designed using compressor adders is a solution to decrease the power consumption and area of the FIR filters and makes the area and power reduction for hearing aid application. The proposed DA based FIR filter architecture is synthesized on 90 nm technology using Synapsis Application Specific Integrated circuit design compiler. The proposed architecture has 45% reduction in area delay product when distinguish with systolic architecture and 10% less ADP when compare with OBC DA architecture. The proposed design is also implemented Field Programmable Gate Array and the results shows that the proposed architecture has less slices than best existing designs. The proposed architecture is used in decimation filter of hearing aids applications using matlab simulink, which removes the unwanted signal.
12 citations
01 Jul 2020
TL;DR: A simple sound classification system that could be used to automatically switch between various hearing aid algorithms based on the auditory related scene and accomplishes high precision with just three to five second duration per scene.
Abstract: Different audio environments require different settings in hearing aid to acquire high-quality speech. Manual tuning of hearing aid settings can be irritating. Thus, hearing aids can be provided with options and settings that can be tuned based on the audio environment. In this paper we provide a simple sound classification system that could be used to automatically switch between various hearing aid algorithms based on the auditory related scene. Features like MFCC, Mel-spectrogram, Chroma, Spectral contrast and Tonnetz are extracted from several hours of audio from five classes like “music,” “noise,” “speech with noise,” “silence,” and “clean speech” for training and testing the network. Using these features audio is processed by the convolution neural network. We show that our system accomplishes high precision with just three to five second duration per scene. The algorithm is efficient and consumes less memory footprint. It is possible to implement the system in digital hearing aid.
11 citations
TL;DR: The objective of this study was to compare the effectiveness of these two strategies on cochlear implant users’ speech-understanding abilities and perceived sound quality in wind noise, and to suggest the conventional strategy to switch to the omnidirectional mode in the wind was undesirable.
Abstract: Objectives Adopting the omnidirectional microphone (OMNI) mode and reducing low-frequency gain are the two most commonly used wind noise reduction strategies in hearing devices. The objective of this study was to compare the effectiveness of these two strategies on cochlear implant users' speech-understanding abilities and perceived sound quality in wind noise. We also examined the effectiveness of a new strategy that adopts the microphone mode with lower wind noise level in each frequency channel. Design A behind-the-ear digital hearing aid with multiple microphone modes was used to record testing materials for cochlear implant participants. It was adjusted to have linear amplification, flat frequency response when worn on a Knowles Electronic Manikin for Acoustic Research to remove the head-related transfer function of the manikin and to mimic typical microphone characteristics of hearing devices. Recordings of wind noise samples and hearing-in-noise test sentences were made when the hearing aid was programmed to four microphone modes, namely (1) OMNI; (2) adaptive directional microphone (ADM); (3) ADM with low-frequency roll-off; and (4) a combination of omnidirectional and directional microphone (COMBO). Wind noise samples were recorded in an acoustically treated wind tunnel from 0° to 360° in 10° increment at a wind velocity of 4.5, 9.0, and 13.5 m/s when the hearing aid was worn on the manikin. Two wind noise samples recorded at 90° and 300° head angles at the wind velocity of 9.0 m/s were chosen to take advantage of the spectral masking release effects of COMBO. The samples were then mixed with the sentences recorded using identical settings. Cochlear implant participants listened to the speech-in-wind testing materials and they repeated the sentences and compared overall sound quality preferences of different microphone modes using a paired-comparison categorical rating paradigm. The participants also rated their preferences of wind-only samples. Results COMBO yielded the highest speech recognition scores among the four microphone modes, and it was also preferred the most often, likely due to the reduction of spectral masking. The speech recognition scores generated using ADM with low-frequency roll-off were either equal to or lower than those obtained using ADM because gain reduction decreased not only the level of wind noise but also the low-frequency energy of speech. OMNI consistently yielded speech recognition scores lower than COMBO, and it was often rated as less preferable than other microphone modes, suggesting the conventional strategy to switch to the omnidirectional mode in the wind was undesirable. Conclusions Neither adopting an OMNI nor reducing low-frequency gain generated higher speech recognition scores or higher sound quality ratings than COMBO. Adopting the microphone with lower wind noise level in different frequency channels can provide spectral masking release, and it is a more effective wind noise reduction strategy. The natural 6 dB/octave low-frequency roll-off of first-order directional microphones should be compensated when speech is present. Signal detection and decision rules for wind noise reduction applications are discussed in hearing devices with and without binaural transmission capability.
2 citations
30 Jan 2020
TL;DR: In this article, the Static Floating Point Sample Rate Converter (SFP-SRC) with Linear Phase Finite Impulse Response (LPFIR) was proposed for hearing aid applications.
Abstract: Designing an electronic circuit with low power and small area are two important concerns for signal processing designers. Though fast emergence of the new technologies and several reviews over signal and speech processing, the difficulty cannot be fulfilled for the hearing impaired people. Many filter bank algorithms have been discussed on the hearing aid design to extend the efficiency. The conventional design of cascaded Direct Truncation (DT) data path is mainly based on the design of Full Precision Static Floating Point. In this paper, we introduce Static Floating Point Sample Rate Converter (SFP-SRC) with Linear Phase Finite Impulse Response (LPFIR) for hearing aid applications. The Sampling Rate Conversion is done before or after the LPFIR filter with upsampling and downsampling factors. In order to increase efficiency of DSP systems, filter bank algorithms need more than one sampling rate. The proposed method provides minimum delay and excellent Signal to Noise Ratio (SNR) performance when compared to Post Truncation (PT) data path. In order to obtain better performance, many experiments have been conducted. The proposed SFP-SRC is suitable for hearing assistance applications. Hence, it is implemented on 1/3 octave analysis filter bank with umc-90nm CMOS technology at 24 KHz.
Patent•
04 Aug 2020
TL;DR: In this article, a digital hearing aid sound field recognition algorithm based on a recurrent neural network and a hardware implementation method is described, where the audio is calculated to extract 16-dimensional characteristic values, and the audio feature value is input into a three-layer RNN for feature classification to obtain a sound field environment classification result; the characteristic parameters of the hearing aid are correspondingly adjusted according to different sound field environments.
Abstract: The invention discloses a digital hearing aid sound field recognition algorithm based on a recurrent neural network and a hardware implementation method. According to the digital hearing aid sound field recognition algorithm, filtering analysis is carried out on audio through an all-phase filter bank, the filter bank divides input audio into 16 channels according to auditory characteristics of human ears, and then the audio is calculated to extract 16-dimensional characteristic values; the 16-dimensional feature value is input into a three-layer recurrent neural network for feature classification to obtain a sound field environment classification result; and the characteristic parameters of the hearing aid are correspondingly adjusted according to different sound field environments. According to the invention, channel decomposition is carried out on an audio signal according to a Bark frequency scale divided by human ear auditory characteristics, the audio characteristic value is extracted from the current sound field environment, the extracted audio characteristic value is classified to identify the category of the current sound field environment, and then the hearing aid is controlled to adjust the appropriate hearing aid mode according to the current sound field environment, so that the speech intelligibility and comfort are improved, and a more appropriate hearing improvement effect is achieved.
Posted Content•
TL;DR: In this article, the authors provide a review of previous researches based on non-uniform finite impulse response (FIR) digital filter bank for hearing aid application using frequency response masking (FRM) technique.
Abstract: Hearing aid is an electroacoustic device used to selectively amplify the audio sounds with an aim to make speech more intelligible for a hearing impaired person. Filter bank is one of the important parts of digital hearing aid where the sub band gains of each filter can be tuned to compensate an individuals unique hearing loss pattern. As the human perception is based on the logarithmic scale, nonuniform filter bank outperforms uniform filter bank. The main advantage of nonuniform filer bank is that it requires less number of sub-band filters, hence resulted in low hardware complexity and cost. Much effort has been devoted to design these nonuniform filter banks for hearing aid applications. This paper aimed to provide a review of previous researches based on nonuniform finite impulse response (FIR) digital filter bank for hearing aid application using frequency response masking (FRM) technique. By reviewing filter banks, we try to find the difference between fixed and variable band filter bank and to give an insight about which method is more suitable for matching most common types of hearing loss. Papers which involved methods of design, theoretical computation and simulation results of filter bank have been reviewed.
Patent•
17 Jul 2020
TL;DR: In this paper, a multichannel digital hearing aid configuration device and method is described, which comprises the steps of acquiring a hearing data matrix T and hearing aid parameters, matching a microphone sensitivity parameter matrix MIC, a receiver sensitivity parameter Matrix REC, an amplifier parameter matrix C and a compensation characteristic value parameter matrix Type, calculating a compression inflection point parameter matrix CT, a compression ratio parameter matrix CR and an output automatic gain control parameter matrix MPO; calculating an average hearing value Taverage; calculating a small sound gain parameter matrix G; configuring hearing aids according to the above
Abstract: The invention discloses a multichannel digital hearing aid configuration device and method. The method comprises the steps of acquiring a hearing data matrix T and hearing aid parameters; matching a microphone sensitivity parameter matrix MIC, a receiver sensitivity parameter matrix REC, an amplifier parameter matrix C and a compensation characteristic value parameter matrix Type; calculating a compression inflection point parameter matrix CT, a compression ratio parameter matrix CR and an output automatic gain control parameter matrix MPO; calculating an average hearing value Taverage; calculating a noise reduction level parameter matrix NR and a feedback suppression level parameter matrix FBC; calculating a small sound gain parameter matrix G; configuring hearing aids according to the above parameters. According to the multi-channel digital hearing aid configuration device and method, the sound pressure level actually needing to be output by the hearing aid by a user is calculated according to the hearing condition of the user, and then parameters needing to be set by the hearing aid are calculated through the type of the hearing aid and matrix parameters of internal components of the hearing aid. Setting work can be completed without the need for a fitting person to have rich experience.
Patent•
20 Oct 2020
TL;DR: In this article, a digital hearing aid parameter adjustment method and device based on big data and cloud space is proposed, which consists of the following steps: acquiring audio data sent by a digital audio aid, sending the audio data to a cloud space on a server; in the cloud space, marking the audio audio data by adopting a preset scene label to obtain an audio data sample; extracting sample features of the audio samples, and training a preset classification model according to the sample features and the scene labels corresponding to the sampled features; grouping the audio datasets according to position information and the time
Abstract: The invention relates to a digital hearing aid parameter adjustment method and device based on big data and cloud space. The method comprises the following steps: acquiring audio data sent by a digital hearing aid; sending the audio data to a cloud space on a server; in the cloud space, marking the audio data by adopting a preset scene label to obtain an audio data sample; extracting sample features of the audio data samples, and training a preset classification model according to the sample features and the scene labels corresponding to the sample features; grouping the audio data according to the position information and the time information, and establishing a corresponding relationship between the position information and the time information and a scene label; receiving the sent real-time audio data; and determining a corresponding scene label according to the corresponding relationship, and performing parameter adjustment on the digital hearing aid according to preset adjustmentparameters corresponding to the scene label. By adopting the method, the accuracy of parameter adjustment can be improved.
Patent•
26 Nov 2020
TL;DR: In this paper, a Digital Hearing Aid (DHA) is adapted for integration with an audiometer, comprising of a power supply, a low pass filter, a sampler configured for conversion of analog signal to digital signal, a sound manipulation unit, a microcontroller including audio peripherals i.e. microphone and audio DAC with speaker and further configured for sound manipulation and output digital processed signal.
Abstract: A Digital Hearing Aid (DHA) adapted for integration with an audiometer, comprising of a power supply, a low pass filter, a sampler configured for conversion of analog signal to digital signal, a sound manipulation unit, a microcontroller including audio peripherals i.e. microphone and audio DAC with speaker and further configured for sound manipulation and output digital processed signal wherein the sound manipulation unit further includes a noise reduction filter, a frequency shaper and an amplitude shaper.
Patent•
10 Nov 2020
TL;DR: In this article, a variable-step-size adaptive echo cancellation device and echo cancellation method for digital hearing aid systems is described, which consists of a single-frequency sound detector, a step size controller, and an adaptive filter.
Abstract: The invention discloses a variable-step-size hearing aid adaptive echo cancellation device and echo cancellation method, and the device comprises a single-frequency sound detector, a step size controller, and an adaptive filter. The single-frequency sound detector is used for carrying out the spectrum energy analysis of an error signal sample, calculating a current hearing aid system state parameter, and transmitting the parameters to a step length controller; the step length controller is used for judging the state of the system according to the system state parameter obtained from the single-frequency sound detector and the normalization error mean value, calculating a time-varying step length parameter of the adaptive filter, and transmitting the time-varying step length parameter to the adaptive filter; and the adaptive filter is used for filtering the cached far-end signal sample of the loudspeaker, calculating an estimated echo signal and outputting the estimated echo signal, anditeratively updating the adaptive filter according to the time-varying step parameter calculated by the step controller. The device and method solve the problems that an adaptive filter is low in convergence rate and too high in algorithm complexity and is difficult to realize in the prior art, and are suitable for a digital hearing aid system.
Patent•
14 May 2020
TL;DR: In this paper, a digital hearing aid is implemented as a smartphone application as an alternative to ASIC-based hearing aids, which provides user-configurable processing for background noise suppression and dynamic range compression.
Abstract: Hearing aids for persons with sensorineural hearing loss aim to compensate for degraded speech perception caused by frequency-dependent elevation of hearing thresholds, reduced dynamic range, abnormal loudness growth, and increased temporal and spectral masking. A digital hearing aid is implemented as a smartphone application as an alternative to ASIC-based hearing aids. The implementation provides user-configurable processing for background noise suppression and dynamic range compression. Both processing blocks are implemented for real-time processing using single FFT-based analysis-synthesis. A touch-controlled graphical user interface enables the user to set and fine-tune the processing parameters in an interactive and real-time mode.
22 Oct 2020
TL;DR: The proposed work can check noise with speech signal and observe their behavior with noise, and removes the noise through different noise removal techniques and observes again and check which technique give us a better result.
Abstract: Hearing loss is the major problem due to which a lot of people suffer across the world. Hearing loss occurs when someone loses the ability of hearing which may occur in any part of the ear. There are different types of hearing aid devices available to enhance the speech signals for the impaired people. In the earliest of times people often used the analog hearing aid devices which worked on the principle of using an amplifier. Analog Hearing aid amplified the incoming sound signal to the ear. Analog Hearing aid did not use any techniques to reduce the noise of the speech signals. For special solutions of individual patients were used as a digital hearing aid, which is more fitting flexibility and ability to program the hearing aids and able to adjust in different conditions rather than using the Analog hearing aids. The digital hearing aids can be programmed to match the patents hearing loss individually according to a specific frequency. The aids are programmed using the human audiogram. Digital hearing aid can be work with a very low power battery, approximately in mW. The proposed work can check noise with speech signal and observe their behavior with noise. After adding noise it removes the noise through different noise removal techniques and observes again. After the noise removal with the noise removal technique we observe again and check which technique give us a better result.