Discussion

From Table 3.1 to Table 3.4 we can find that SNRseg and PESQ are directly proportional to AR order. Intuitively, cleaner speech signals generate more accurate estimates of AR coefficients. Therefore we can model the speech signal more accurately. In these examples the SNRseg and PESQ grades of the two methods saturate on AR3. In my experience different noisy speech signals saturates on different AR orders. Generally AR4 is good enough to model the speech signals.

In Fig. 3.11 and Fig. 3.12 we contaminate five different speech samples and use the two methods to enhance them. Obviously in speech sample 1 and 3 the fixed AR method is much better than frame-based method. On the other hand the frame-based method is better in the other speech samples. Generally the two methods can enhance noisy speech except the few samples. For example in speech sample 4 the fixed AR method’s PESQ is worse than unfiltered noisy speech. But in the subjective evaluation of speech quality, the filtered speech still sounds better than the unfiltered noisy speech does. Since PESQ is an objective measure to simulate the evaluation of the subjective speech quality, it is a valuable reference but not an absolute standard. In order to further compare the significant difference in speech quality resulting from these algorithms, we should do the subjective listening tests [31].

In this part, we discuss the computational complexities of Kalman filtering. The following result is derived from [27]. First, we define three terms for measuring complexity:

MPU, multiplications per unit of time; DVU, divisions per unit of time; and APU, additions per unit of time. According to (3.1), speech is modeled as AR(p). If p≥ the AR 1

MPU、p APU. The Kalman filter described in (3.6)-(3.9) requires 3p²+2p MPU, p DVU, and 3p²+ APU. Totally the frame-based AR Kalman filter needs 2 6p²+3p MPU、3p²+ + ADU and p DVU. On the other hand the Kalman filter with fixed AR p 2 coefficients needs 3p²+2p MPU、3p²+ APU and p DVU. We list the complexities 2 of Kalman filtering and spectrum subtraction for white noise cancellation in Table 3.5.

Obviously in the single band the AR3 and AR4 Kalman filters are more complicated than spectrum subtraction.

Table 3.5 Overall complexities of a Kalman filter.

MPU DVU ADU

AR 3p ² 0 0

Q p 0 p

Kalman 3p²+2p p 3p²+ 2

Table 3.6 Complexities of Kalman filtering and spectrum subtracton for white noise cancellation.

Fixed AR Frame-based AR

AR1 AR2 AR3 AR4 AR1 AR2 AR3 AR4 Spectrum Subtraction

MPU 5 16 33 56 9 30 63 108 0

DVU 1 2 3 4 1 2 3 4 0

ADU 5 14 29 50 6 16 32 54 119

From Table 3.1 to Table 3.4 the performances of Kalman filters are good in every AR order and SNR. But the complexities of Kalman filters are too high. So in the single band Kalman filters may not be able to process noisy speech in real time. As a result, it may not be suitable for the white noise cancellation in the hearing aids system. We will evaluate the possibility of multi-band Kalman filter for white and color noises cancellation to achieve a balance between the performance and low power/cost in the future.

Chapter 4

Conclusions and future work

4.1 Conclusions

We have described a platform to facilitate research for the development of acoustic feedback algorithm on the basis of an ITC hearing instrument placed in-situ. We used sweep stimulus method to measure the broad band frequency response of the EFP and obtained its equivalent impulse response. Then we take a 40dB gain to get the closed-loop response and make use of the Nyquist criterion to obtain the most possible frequency location of inducing oscillation. Our measurement results are helpful to the design and realization of the feedback cancellation algorithm.

We introduce the Kalman filter and apply it to the white noise cancellation. For the complexity of estimating AR coefficients, we propose a simpler method to reduce the computation of AR coefficients. First we estimate the AR coefficients from a clean speech, and then we use the Kalman filter with the fixed AR coefficients to filter the noisy speech.

We canceled some noise and get good grades of PESQ. Then we compare the complexities of them and spectrum subtraction. I think that the single band Kalman filtering is not suitable for the white noise cancellation in hearing aid system because of its high complexity. As a result we will evaluate the combining of Kalman filter and the existing analysis/synthesis filter bank as the future development in order to achieve a balance between the performance and low power/cost.

4.2 Future work

There are several possible extensions for our researches:

(1) Use the platform to measure the EFP in other situations, such as jaw movements or handset proximity and our future hearing aids.

(2) Evaluate the possibility of using state augmentation or other method for Kalman filter to cancel the color noise.

(3) Combine the Kalman filter and the existing analysis/synthesis filter bank to achieve the balance between the performance and low power/cost.

(4) Do the subjective listening tests conducted according to the ITU-T P.835 [31],[32].

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About the Author

姓 名：陳建男 Chien-Nan Chen 出 生 地：高雄市

出生日期：1983. 1. 27

學 歷：

1989. 9 ~ 1990. 6 高雄市立愛群國小 1990. 9 ~ 1995. 6 高雄市立樂群國小 1995. 9 ~ 1998. 6 高雄市立光華國中 1998. 9 ~ 2001. 6 高雄市立高雄中學

2001. 9 ~ 2005. 2 中正大學 電機工程學系 學士