Low power real-time programmable DSP development platform for digital hearing aids

Stetzler, Trudy; Magotra, N.; Gelabert, P.; Kasthuri, Praveen; Bangalore, S.

doi:10.1109/icassp.1999.758407

Cited by 9 publications

(9 citation statements)

References 14 publications

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“…To give a generic characterization of the technique as a function of processing load, we used a series of finite impulse response (FIR) filters applied to the input buffer repeatedly as a dummy processing load. In practice, each channel (left and right) uses between 1 and 50 FIR filters with 50 to 200 taps for frequency shaping, with 14 filters being common [21]. Similar filtering techniques are used for the other types of processing.…”

Section: Methodsmentioning

confidence: 99%

“…The music test [18] [21]. To give a generic characterization of the technique as a function of processing load, we used a series of finite impulse response (FIR) filters applied to the input buffer repeatedly as a dummy processing load.…”

Section: Methodsmentioning

confidence: 99%

“…At any given time the hearing aid is simultaneously transferring samples from the A/D converter to an input buffer, processing data from the previously sampled frame, and transferring samples from the previously processed buffer to the D/A converter. The 20 ms frame size is long enough to produce stationary signals, while not adding noticeable delay [21].…”

Section: Hearing Aid Modelmentioning

confidence: 99%

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Power reduction by varying sampling rate

Dieter

Datta

Kai

2005

Proceedings of the 2005 International Symposium on Low Power Electronics and Design - ISLPED '05

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The rate at which a digital signal processing (DSP) system operates depends on the highest frequency component in the input signal. DSP applications must sample their inputs at a frequency at least twice the highest frequency in the input signal (i.e., the Nyquist rate) to accurately reproduce the signal. Typically a fixed sampling rate, guaranteed to always be high enough, is used. However, an input signal may have periods when the signal has little high frequency content as well as periods of silence. When the input signal has no perceptible high frequency components, the system can reduce its sampling rate, thereby reducing the number of samples processed per second, allowing the CPU speed to be scaled down without reducing output quality. This paper describes how to reduce power consumption in DSP applications by varying the amount of processing based on the input signal, and reports results of experiments with a prototype implementation. Experiments with a prototype show that when the system performs little processing, the added overhead of the variable sampling rate technique increased power consumption. When the system performs more processing, 18 FIR filters per frame, the power consumption was reduced to 40 % of the power required for a static sampling rate, while not reducing sound quality.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Hearing Aid Modelmentioning

confidence: 99%

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Power reduction by varying sampling rate

Dieter

Datta

Kai

2005

Proceedings of the 2005 International Symposium on Low Power Electronics and Design - ISLPED '05

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“…O objetivo geral deste trabalho é fazer a simulação do algoritmo RACE, proposto por Stetzler (1999) [5] para redução de ruído. O software que será utilizado para a análise preliminar do desempenho deste sistema, é o MATLAB®dado sua disponibilidade pela Universidade.…”

Section: Introductionunclassified

Sistema de redução de ruído de baixo custo para sistemas de auxílio à audição: algoritmo RACE.

Miyuki Kaihatu,

Coelho Borges

2022

Anais Do XIII Computer on the Beach - COTB'22

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Hearing impairment affects about 1.5 billion people worldwide,10.7 million of which only in Brazil. As a solution to maximizehearing capacity, the use of hearing aids is commonly indicated.However, the adherence of these devices is low, largely due to thelow sound quality in noisy environments and their high cost. Tominimize these obstacles, several studies and research are carriedout both on the hardware part and on noise reduction techniques.The technique addressed in this work is the Real Time AdaptativeCorrelation Enhancer algorithm, an adaptive filter that uses theautocorrelation coefficients of the noisy audio signal to calculatethe coefficients of Wiener’s FIR (Finite Impulse Response) filter. Aspreliminary results, the simulation of the algorithm in MATLAB®ispresented to discuss the performance of the algorithm. As futurestudies, it is planned to implement the algorithm on the TexasInstruments®MSP432 microcontroller low-cost development board,to analyze the algorithm’s performance with samples in real time.

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“…Various algorithms and processing schemes have been reported for digital hearing aids to overcome the problems faced by the hearing impaired individual [16,17]. Off late studies have focused on addressing the issue of broadened auditory filters [11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Critical-band based compression — an insight into the future of digital hearing aids

Vikrant

Krishna

Rajashekhar

et al. 2008

2008 International Conference on Audio, Language and Image Processing

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Patients with sensorineural hearing impairment often experience difficulty in understanding speech. This can be attributed to the widened auditory filters, which results in greater spectral spread across the critical bands and thus causing increased spectral masking. Off late various methods have been used to compress the signal towards the center of the critical band. In our study we have used gammatone filters to split the speech signal into various bands and each critical band was compressed along the frequency axis. 30 hearing impaired subjects participated in a perceptual judgment and speech identification task. From the results of both the tasks we confirmed that processed speech became more intelligible, thus highlighting the need for devising a processing algorithm which can achieve the compression of signal along the frequency axis in real-time to help patients with sensorineural hearing loss to improve the perceived speech intelligibility.

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Low power real-time programmable DSP development platform for digital hearing aids

Cited by 9 publications

References 14 publications

Power reduction by varying sampling rate

Power reduction by varying sampling rate

Sistema de redução de ruído de baixo custo para sistemas de auxílio à audição: algoritmo RACE.

Critical-band based compression — an insight into the future of digital hearing aids

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