Students: Avihay Barazany Royi Levy Supervisor: Kuti Avargel In Association with: Zoran, Haifa

Transcription

1 Students: Avihay Barazany Royi Levy Supervisor: Kuti Avargel In Association with: Zoran, Haifa Spring 2008

2 Introduction Problem Formulation Possible Solutions Proposed Algorithm Experimental Results Conclusions

3 Digital still cameras are widely used for video and audio recordings. When activating the zoom lens-motor during these recordings, the noise generated by the motor may be recorded by the camera's microphone. This noise may be extremely annoying and significantly degrade the perceived quality and intelligibility of the desired signal.

4 Introduction cont.

5 s t Let x( n), d ( n), d ( n) denote the speech signal, background stationary noise, and zoom motor (nonstationary) noise, respectively. Let signal. s t y( n) x( n) d ( n) d ( n) be the microphone Main goal: to derive an estimator clean speech signal. xn ˆ( ) camera microphone yn ( ) xn ˆ( ) x s d t d for the

6 To solve this problem, many digital-cameras manufacturers disable the option of activating the lens motor during audio recordings. Adaptive solution Add a reference microphone and implement an adaptive algorithm for cancelling the motor noise in real-time. Spectral enhancement Using spectral enhancement techniques for estimating the motor noise spectrum and enhancing the speech signal.

7 Spectral Enhancement Techniques The spectral enhancement approach is operated on the time-frequency domain. Let the observed signal be: y( n) x( n) d( n) The goal is to estimate the spectral coefficient of the speech signal. Let xn ( ) be the short time Fourier transform (STFT) of, i.e., 2 - j km N X w( ll - m) x( m) e X m

8 Spectral Enhancement Techniques cont. The desired estimate of is : X G Y where the gain function minimizing a cost-function: is achieved by There are different ways to measure the distortion function. The commonly used distortion functions are: ˆ X G 2 d X, Xˆ X Xˆ 2 or, ˆ log log ˆ 2 d X X X X ˆ arg min E d X, Xˆ G

9 Spectral Enhancement Techniques cont. The disadvantage of the above mentioned algorithms, is their difficulty to handle with highly non-stationary noises. Input Signal OMLSA Only

10 The algorithm is based on paper: A., Abramson, I., Cohen, Enhancement of Speech Signals Under Multiple Hypotheses using an Indicator for Transient Noise Presence, 2007 Since the problem consists of 2 different types of noises, the definition of the observed signal is: s t y( n) x( n) d ( n) d ( n) And d s s X, Y, D, D t t ( n), d ( n) accordingly. are the STFT of x( n), y( n),

11 Since the motor noise not always present, we define the following 4 hypothesis: H : Y X D s 1s H : Y X D D s t 1t H : Y D s 0s H : Y D D s t 0t H1 H0 : speech is more dominant than noise. : noise is more dominant than speech.

12 Let j, j 0,1 denote the detector decision in the time-frequency bin, : 0 1 transient is a noise component transient is a speech component Let C10, C01 denote the cost of false-alarm / missdetections, respectively. The algorithm assumes an indicator signal for the motor noise in the time frame l. Indicator

13 Let A X, R Y. The criterion for the estimation of the speech signal under the decision where Aˆ j Aˆ arg m in C p H H, Y : 1 j 1s 1t j E d X, Aˆ Y, H H 1s 1t,, ˆ j C p H H Y d G R A 0 j 0s 0t min 2 d( x, y) log x log y.

14 Based on above definitions, the gain function is defined : Aˆ G (, ) Y 1a where G (, ) j G G LSA Y 2 s, t, x, s, t, j min (, ) :a-posteriorisnr :a-priorisnr When no motor noise exists (indicator= 0 ), we will use the conventional OMLSA: a P( H ). 1 a

15 t D s D Y Xˆ ISTFT xn ˆ( ) X G j, gain func. computation

16 MCRA ˆ ds Speech varience ˆx estimate Y Motor Noise Estimate ˆ dt Comp. G j, Probability P H 1 Estimator G min computation G min

17 Parameters Setup: Several SNR s of motor noise and speech were experimented. For each recording several values were considered. Different parameter sets were tried out until the optimized ones were found. The performance of the proposed approach was compared to those of the conventional OMLSA. G f

18 Gf=-15dB Gf=-20dB Input Signal OMLSA Only

19 Gf=-15dB Gf=-25dB Input Signal OMLSA Only

20 Gf=-12dB Gf=-20dB Input Signal

21 Gf=-15dB Gf=-25dB Input Signal

22 Gf=-15dB Gf=-25dB Input Signal

23 Gf=-15dB Gf=-25dB Input Signal

24 Gf=-15dB Gf=-20dB Input Signal

25 An algorithm for suppressing lens motor noise has been introduced. An optimal estimator, is derived, while assuming some indicator for the motor-noise presence in the time domain. A-priori motor noise spectrum estimate is acquired. A substantial suppression of the motor noise is achieved, without degrading the perceived quality of the desired signal. The proposed algorithm is computationally efficient.

26 The Signal & Image processing lab for technical support during the entire work process. The Control & Robotics lab for assistance with assembling of the camera module together with an I/O control card. For all the guidance and academic support by Kuti Avargel.

27 I. Cohen and B. Berdugo, Speech Enhancement for Non- Stationary Noise Environments, Signal Processing, Vol. 81, No. 11, pp , Nov I. Cohen and B. Berdugo, Noise estimation by minima controlled recursive averaging for robust speech enhancement, Signal Processing, Vol. 9, Issue 1, pp , Jan A. Abramson and I. Cohen," Enhancement of Speech Signals Under Multiple Hypotheses Using an Indicator for Transient Noise Presence " Proc. 31th IEEE Internat. A., Abramson, I. Cohen, Simultaneous Detection and Estimation Approach for Speech Enhancement, Audio, Speech, and Language Processing, IEEE Transactions on Vol. 15, Issue 8, pp , Nov

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29 The a-priori estimation for the motor noise is achieved using an average of early acquired recordings. 0 The algorithm updates the initial estimation according to pre-determined regions. The result is the desired : 1 k ˆ 0(, ) t H ˆ 0 : ˆ t l, 1 k H : ˆ, (, ) 1 ˆ ( l 1, k ) t 0 The noise is classified by the criteria: Motor noise level higher than speech level ˆt 2 ( l 1, k) 1 Y l, k s( l, ) t H. 0

30 Region classification: Method of classification: Frequencies that are out of speech band [>4 KHz ], are assumed to be in H. 0 High amplitude harmonies in the motor noise estimation are classified as as well. H 0 High amplitude harmonies are determined by an empiric threshold. The rest of the spectrum is classified as H. 1

31 In general the speech spectral estimation is calculated by subtracting the motor noise estimation and the background noise estimation from the observed signal. 2 2 ˆ max G ˆ ˆ ˆ 1, k, l 1 k Yl 1, k 1 Y, s t, Previousframe estimate Current frame estimate 2 xl, k LSA l, min

32 Using the MCRA algorithm the noise spectrum is estimated. Let ˆs, Let denote the conditional speech presence probability, therefore the update equation for is : where p ' be the noise spectrum estimation. 2 ˆ ( l 1, k) ( l, k) ˆ ( l, k) 1 ( l, k) Y l, k s d s d p l k ( l, k) 1 ',. d d d ˆs, Let Sr ( l, k) S l, k / Smin l, k denote the ratio between the local energy of the noisy signal and its derived minimum. The decision rule is: S ( l, k), threshold value. r H 1 H 0

33 In order to suppress the noise (stat. & transients) when speech is absence, minimizing the next equation yields the solution above: min arg min E Gmin s, t, Gf s, G G min Let denote the constant attenuation under speech absence: G min G f s, s, t,

34 Let 1 1 exp P H 1 qˆ 1 qˆ 1 qˆ( l, k) 1 P ( l, k) P ( l, k) P ( l) q Where is the estimator for the a-priori signal absence probability. ˆ local global frame qˆ is larger if either previous frames or recent neighboring frequency bins do not contain speech.