A real-time kepstrum approach to speech enhancement and noise cancellation

Identification of acoustic path transfer function component in beamforming structure

“…It is noted that when minimum phase filter is applied, only one non-minimum phase component is represented by estimate of adaptive FIR filter. For the test, kepstrum (complex cepstrum) method [7] as minimum phase filter, its inversed form as inverse minimum phase filter and recursive least squares (RLS) algorithm based adaptive FIR filter [1] have been used as third order minimum phase filter and third order inverse minimum phase filter and first order adaptive FIR filter respectively .…”

Real-Time Noise Cancelling Approach on Innovations-Based Whitening Application to Adaptive FIR RLS in Beamforming Structure

“…The application can be found from (Moir & Barrett, 2003;Jeong & Moir, 2008), where the unknown system has been estimated as the ratio ( 1 H / 2 H ) of two acoustic transfer functions between each microphones and noise source. Kepstrum filter is used as estimate of unknown system and it is applied in front of SS (sum and subtract) functions in beamforming structure (Jeong & Moir, 2008 The beamforming structure contains SS functions, where it is used as signal separator and enhancer by summing and subtracting the signals of the each microphones input (Griffiths & Jim, 1982). An adaptive filter 1 is placed in front of SS functions and used as speech beamforming filter (Compernolle, 1990).…”

“…As desribed above, the techniques have been developed on the basis of above described methods and the structures. From the above analysis, kepstrum noise cancelling technique has been studied, where the kepstrum has been used for the identification of acoustic transfer functions between two microphones and the kepstrum coefficients from the ratio of two acoustic transfer functions have been applied in front of adaptive beamforming structure for noise cancellation and speech enhancement (Jeong & Moir, 2008). Furthermore, by using the fact that the random signal plus noise may be represented as output of normalized minimum phase spectral factor from the innovations white-noise input (Kalman & Bucy, 1961), the application of an innovations-based whitened form (here we call it as inverse kepstrum) has been investigated in a simulation test, where front-end inverse kepstrum has been analyzed with application of cascaded FIR LMS algorithm (Jeong, 2009) and also FIR RLS algorithm (Jeong, 2010a;2010b), both in ANC structure for noise cancellation.…”

“…7. As an alternative approach, the system identification based kepstrum method has been studied in beamforming structure (Jeong & Moir, 2008 …”

Minimum phase whitening filter to speech beamforming technique

“…For a real-time noise cancelling application, kepstrum (also called as complex cepstrum) filtering method [3] has been introduced, where the estimates of noise statistics are updated during the noise alone period and then during the desired signal plus noise period, the frozen estimates are applied to cancel the noise from noisy signal. It has been found from kepstrum filtering method that front-end minimum phase kepstrum filter estimates minimum phase terms from overall acoustic transfer function in beamforming structure so that a rear-end adaptive filter only requires estimate of remaining non-minimum phase terms so that it could reduce a processing time on adaptive filter for real-time implementation [4].…”