For CI devices, the generalized sidelobe canceller is overly complicated and requires too many microphones, conditions that exceed the capabilities of current CI devices. Specifically, the generalized sidelobe canceller and delay beamforming use multiple microphones to record signals for spatial filtering. The microphone array technique considers the signal orientation information and focuses on directional speech enhancement. Algorithm performance sharply weakens when the noise is non-stationary, or under typical situations with music or ambient speech noise. Typical single-channel approaches, such as the spectral subtraction, Wiener filtering, and subspace approach, are based on estimations of the power spectrum or higher- order spectrum, assume the noise to be stationary, and use the noise spectrum in the nonspeech frame to estimate the speech-frame noise spectrum. Spectral estimation methods are the most widely used single-channel techniques. Speech-enhancement methods include single- and multichannel techniques. This array beamforming method promises to be more effective for situations in which the desired voice and ambient noise originate from different directions, the usual work environment for CI devices. More recent efforts have focused on the microphone array technique. Most previous studies on recognition improvement have focused on the coding strategy, design of the electrode array, and stimulation adjustment of pitch recognition, as well as on the virtual electrode technique and optical CIs. The SNR in the typical daily environment is about 5–10 dB, which results in <50% sentence recognition for CI users in a normal noise environment. For 50% sentence understanding, the required signal to noise ratio (SNR) is between 5 and 15 dB for CI recipients, but only −10 dB for normal listeners. The clinical cochlear implant (CI) has good speech recognition under quiet conditions, but noticeably poor recognition under noisy conditions.
![speech timer estimator speech timer estimator](https://www.jokejive.com/images/jokejive/35/3544a04168aef6ff5fb94d5aba0ef3ec.jpeg)
And signal distortion results indicate that this algorithm is robust with high SNR improvement and low speech distortion. Excellent performance of the proposed algorithm was obtained in the speech enhancement experiments and mobile testing. The speech-enhancement results showed that our algorithm can suppresses the non-stationary noise with high SNR. The hardware platform was constructed for the experiments. The performance of the proposed algorithm is analyzed and further be compared with other prevalent methods. We also analyze the algorithm constraint for noise estimation and distortion in CI processing. The approximation of the directivity coefficient is tested and the errors are discussed. The broadband adjustment coefficients were added to compensate the energy loss in the low frequency band.
![speech timer estimator speech timer estimator](https://www.rehabmart.com/include-mt/img-resize.asp?path=/imagesfromrd/ma-703026reizen_talking_calculator.jpg)
![speech timer estimator speech timer estimator](https://image.slidesharecdn.com/timer-141223000312-conversion-gate02/95/timer-2-638.jpg)
For actual parameters, we use Maxflat filter to obtain fractional sampling points and cepstrum method to differentiate the desired speech frame and the noise frame. The proposed algorithm was realized in the CI speech strategy. The directivity coefficient was estimated in the noise-only intervals, and was updated to fit for the mobile noise. The spectrum estimation and the array beamforming methods were combined to suppress the ambient noise. The experimental results indicate that this method is robust to directional mobile noise and strongly enhances the desired speech, thereby improving the performance of CI devices in a noisy environment. To suppress the directional noise, we introduce a speech-enhancement algorithm based on microphone array beamforming and spectral estimation. Improvement of the cochlear implant (CI) front-end signal acquisition is needed to increase speech recognition in noisy environments.