Abstract
Kalman filtresinin, özellikle GPS ve Navigasyon uygulamalarında sunduğu üstün tahmin yeteneği, son
yıllarda ses sinyallerinin işlenmesinde de kullanılmaya çalışılmıştır. Kalman filtresi en etkili ses iyileştirme
yöntemlerinden biridir ancak, Kalman filtresi ile ses sinyallerini iyileştirebilmek için bir takım
parametrelerin bilinmesi gerekmektedir. Temiz sinyale ait AR (Autoregressive) katsayıları ve gürültüye ait
kovaryans matrisi, Kalman filtresinin başarısını çok büyük ölçüde etkileyen ve bilinmesi gereken
parametrelerdir. Pratikte mevcut olan sadece gürültülü sinyal olduğu için bu parametrelerin tahmin edilmesi
oldukça zordur ve hala bu konu üzerinde çalışmalar devam etmektedir.
Bu çalışmada, farklı tipteki gürültülerle bozulmuş sinyallere, Spektral çıkarma, Wiener filtresi ve Kalman
filtresi ayrı ayrı uygulanmıştır.Kalman filtresi için gerekli olan katsayılar temiz sinyal kullanılarak
hesaplanmıştır. Daha sonra Spektral çıkarma ile birleştirilmiş Kalman filtresi uygulanmıştır. Kalman filtresi
için gerekli olan parametreler Spektral çıkarma yöntemi ile iyileştirilmiş sinyal kullanılarak belirlenmiştir.
Uygulama sonuçları, objektif bir ölçüm olan SNR değerleri baz alınarak karşılaştırılmıştır.
Elde edilen sonuçlar; birleştirilmiş Kalman filtresinin Wiener filtresine ve Spektral çıkarmaya oranla daha
iyi bir SNR artışı sağladığını göstermiştir. Ayrıca birleştirilmiş Kalman filtresinin Spektral çıkarmadan
kaynaklanan müzikal gürültüyü bastırdığı da gözlemlenmiştir.
Abstract
Speech enhancement techniques aim to improve the
quality or intelligibility of speech signals
contaminated with background noise and can be
implemented both in time and frequency domains.
Spectral subtraction, one of the most feasible
methods in practice, is an effective technique to
enhance the noisy speech signals. However, a
residual noise called musical noise occurs with the
estimated speech signal and this is the major
inconvenience of Spectral subtraction.
Wiener filter is an alternative approach for speech
enhancement in the manner of Spectral subtraction
filter. The drawback of the Wiener filter is the fixed
frequency response at all frequencies and the
requirement to estimate the power spectral density
of the clean signal and the noise prior to filtering.
Kalman filtering is also one of the most effective
methods in speech enhancement. In recent years,
due to its magnificent accurate estimation
characteristics especially in the research field of
navigation and GPS, researchers tried to manipulate
its advantages for useful purposes in signal
processing.
However, to improve the speech signals with the
Kalman filter, some parameters such as the
autoregressive (AR) coefficients of the clean signal
and the noise covariance matrix must be known.
Determining the AR coefficients of clean speech
signal plays a crucial role for the success of the
Kalman filter while the only noisy observations are
available. In such condition it is very difficult to
estimate these parameters and today researches on
this issue are ongoing.
In this study, the parameters necessary to implement
the Kalman filter is determined using Spectral
subtraction. First of all, Spectral subtraction,
Wiener filter and Kalman filter is analyzed
respectively. Then all three methods mentioned
above are carried out for speech signals corrupted
with different types of noise. Finally, Kalman filter
combined with Spectral Subtraction proposed in this
study is applied to those signals and all results are
compared based on output SNR values as an
objective measurement for the enhancement
performance.
The results achieved in this study has shown that, if
the AR cofficients of the original signal and noise
variance is known Kalman filter is sufficient alone
for the enhancement of noisy speech signals. In
addition, musical noise which occurs with the
methods based on the noise spectrum estimation,
does not occur with the Kalman filtering. However,
the assumption that these parameters are known is
not workable in practice. Therefore, the AR
coefficients are calculated by using the signal
enhanced with Spectral subtraction and also noise
variance is calculated by subtracting the enhanced
signal from the noisy signal. In the last stage,
Kalman filter was applied to the noisy signal using
the parameters determined with Spectral
subtraction.
Considering the obtained results, combined Kalman
filter provided a better SNR improvement compared
to the Wiener filter and Spectral subtraction. Also
combined Kalman filter suppressed the musical
noise that occurred owing to Spectral subtraction.
When the SNR values are taken into account, it is
seen that the Kalman filter alone provided a better
SNR improvement than combined Kalman filter.This
is because of using the original signal while
calculating the AR parameters for the Kalman filter
alone.
Details
| Other ID | JA67EH66HV |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | June 1, 2012 |
| Submission Date | June 1, 2012 |
| Published in Issue | Year 2012 Volume: 3 Issue: 1 |