【正文】 ( λ, k) = Bmin (λ, k). Pmin (λ, k)Figure3 Plot of true noise spectrum and estimated noise spectrum usingContinuous Spectral Minimum Tracking Arrows indicate regions wherenoise is overestimated [12] Continuous Spectral MinimumOne of the drawbacks of the minimal tracking employed in the MS algorithm is its inability to respond to fast changes of the noise spectrum [12]. A different method for tracking the spectral minima was proposed in [10]. In contrast to using a fixed window for tracking the minimum of noisy speech is in [5] the noise estimate is updated continuously by 。. We can obtain an estimate of the power spectrum of the noise by tracking the minimum of P(λ,k).Our finite window smoothing constant α chosen experimentally not too low or too high. There are two main issues with the spectral minimal – tracking approach the existence of a bias in the noise estimate and the possible overestimate of the noise level because of inappropriate choice of the smoothing constant. More accurate noise estimation algorithm can be developed by deriving a bias factor to pensate for the lower noise values and by incorporating a smoothing constant that is not fixed but varies with time and frequency. The noise estimation algorithm using MS is summarized as below [12]. For each frame λ do following steps1. Compute the shortterm periodogram |Y(λ, k)|178。 fluctuates very rapidly over time, hence 1st under recursive version of periodogram can be used asP (λ, k) = α P (λ – 1, k) + (1α) |Y (λ, k) |178。 is the periodogram of Noise signal. Because of this assumption, we can estimate the noise power spectrum by tracking the minimum of the periodogram |Y(λ, k) |178。 is the periodogram of noisy speed signal, |X (λ, k)|178。 + |D(λ, k) |178。 speech coders and many other speech processing systems. In most speech enhancement algorithms it is assumed that an estimate of noise spectrum is available. Noise estimate is critical part and it is important for speech enhancement algorithms. Performance of speech enhancement algorithms depends on correct estimation of noise. Simple approach to estimate the noise spectrum of the signal using a Voice Activity Detector (VAD) another approach to estimate the noise using different noise estimation algorithms Noise estimation algorithms that continuously track the noise spectrum. It is challenging task to estimate the noise spectrum even during speech activity hence Researcher developed many noise estimation algorithms which are explained in next section.2. Voice Activity DetectionSimple approach to estimate and update the noise spectrum during the silent segments of the signal using a Voice Activity Detector (VAD). The process of discriminating between the voice activity that is speech presence and silence that is speech absence is called voice activity detection. VAD algorithms typically extract some type of feature (. short time energy, zero crossing etc.) from the input signal and pared against threshold value, usually determined during speech absent period. Generally output of VAD algorithms is binary decision on a framebyframe basis having frame duration 2030 msec. A segment of speech is declared to contain voice activity (VAD = ‘1’) if measured value exceed a predetermined threshold otherwise it is declared a noise (VAD = ‘0’) figure 1shows VAD decisions. Several VAD algorithms were proposed based on various types of features extracted from the signal. Noise estimation can have major impact on the quality and Intelligibility of speech signal.Figure 1 shows VAD decisions [3]The early VAD Algorithms were based on energy levels and zero crossing [4], Ceptral features [4], the Itakura LPC spectral distance measures and the periodicity measures [2]. Some of VAD Algorithms are used in (GSM) System [3], cellular Networks [3], and digital cordless telephone systems [3]. VAD Algorithms are suitable for discontinues transmission in voice munication systems as they can be used to save the battery life of cellular phones. The majority of the VAD Algorithms encounter problems in low SNR conditions, particularly when the noise is nonstationary [1, 2]. Having an accurate VAD Algorithm in a nonstationary environment might not be sufficient in speech enhancement. Applications, as on accurate noise estimation is required at all times, even during speech activity. In case of Noise estimation algorithms they continuously track the noise spectrum therefore more suited for speech enhancement applications in nonstationary Scenarios.3. Classes of Noise Estimation AlgorithmsThere are three classes of noise estimation algorithms. Minimal tracking Algorithms, Time Recursive Algorithms and Histogram based Algorithms. All algorithms operate in the following fashion. First the signal is analyzed using short time spectra puted from short overlapping frames, typically 2030 msec. Windows with 50% overlap between adjacent frames. Then several consecutive frames called analysis segment are used in the putation of the noise spectrum. Typical time span of this segment may range from 400 msec. to 1 sec. The noise estimation algorithms are based on the assumptions that the analysis segment is too long enough to contain speech pauses and low energy signals segments and the noise present in the analysis segment is more stationary than speech, new assumption is that noise changes at a relatively slower rate than speech. The analysis segment has to be long enough to enpass speech pauses and low energy segments, but it also has to be short enough to track fast changes in the noise level, hence the chosen duration of the analysis segment will result from a trackoff between these two restrictions. Now we will see different classes of noise estimation Algorithms.Figure2 Plot of noisy speech power spectrum and local minimum [10] Minimal – Tracking AlgorithmsMinimal Tracking Algorithms are based on the assumption that the power of the noisy speech signal in individual frequency ba