摘要:
A model application unit calculates linear prediction coefficients of a multi-step linear prediction model by using discrete acoustic signals. Then, a late reverberation predictor calculates linear prediction values obtained by substituting the linear prediction coefficients and the discrete acoustic signals into linear prediction term of the multi-step linear prediction model, as predicted late reverberations. Next, a frequency domain converter converts the discrete acoustic signals to discrete acoustic signals in the frequency domain and also converts the predicted late reverberations to predicted late reverberations in the frequency domain. A late reverberation eliminator calculates relative values between the amplitude spectra of the discrete acoustic signals expressed in the frequency domain and the amplitude spectra of the predicted late reverberations expressed in the frequency domain, and provides the relative values as predicted amplitude spectra of a dereverberation signal.
摘要:
A sound source model storage section stores a sound source model that represents an audio signal emitted from a sound source in the form of a probability density function. An observation signal, which is obtained by collecting the audio signal, is converted into a plurality of frequency-specific observation signals each corresponding to one of a plurality of frequency bands. Then, a dereverberation filter corresponding to each frequency band is estimated by using the frequency-specific observation signal for the frequency band on the basis of the sound source model and a reverberation model that represents a relationship for each frequency band among the audio signal, the observation signal and the dereverberation filter. A frequency-specific target signal corresponding to each frequency band is determined by applying the dereverberation filter for the frequency band to the frequency-specific observation signal for the frequency band, and the resulting frequency-specific target signals are integrated.
摘要:
A model application unit calculates linear prediction coefficients of a multi-step linear prediction model by using discrete acoustic signals. Then, a late reverberation predictor calculates linear prediction values obtained by substituting the linear prediction coefficients and the discrete acoustic signals into linear prediction term of the multi-step linear prediction model, as predicted late reverberations. Next, a frequency domain converter converts the discrete acoustic signals to discrete acoustic signals in the frequency domain and also converts the predicted late reverberations to predicted late reverberations in the frequency domain. A late reverberation eliminator calculates relative values between the amplitude spectra of the discrete acoustic signals expressed in the frequency domain and the amplitude spectra of the predicted late reverberations expressed in the frequency domain, and provides the relative values as predicted amplitude spectra of a dereverberation signal.
摘要:
A sound source model storage section stores a sound source model that represents an audio signal emitted from a sound source in the form of a probability density function. An observation signal, which is obtained by collecting the audio signal, is converted into a plurality of frequency-specific observation signals each corresponding to one of a plurality of frequency bands. Then, a dereverberation filter corresponding to each frequency band is estimated by using the frequency-specific observation signal for the frequency band on the basis of the sound source model and a reverberation model that represents a relationship for each frequency band among the audio signal, the observation signal and the dereverberation filter. A frequency-specific target signal corresponding to each frequency band is determined by applying the dereverberation filter for the frequency band to the frequency-specific observation signal for the frequency band, and the resulting frequency-specific target signals are integrated.
摘要:
The initial values of parameter estimates are set, including reverberation parameter estimates, which includes a regression coefficient used in a linear convolutional operation for calculating an estimated value of reverberation included in an observed signal, source parameter estimates, which includes estimated values of a linear prediction coefficient and a prediction residual power that identify the power spectrum of a source signal, and noise parameter estimates, which include noise power spectrum estimates. Then, the maximum likelihood estimation is used to alternately repeat processing for updating at least one of the reverberation parameter estimates and the noise parameter estimates and processing for updating the source parameter estimates until a predetermined termination condition is satisfied.
摘要:
The initial values of parameter estimates are set, including reverberation parameter estimates, which includes a regression coefficient used in a linear convolutional operation for calculating an estimated value of reverberation included in an observed signal, source parameter estimates, which includes estimated values of a linear prediction coefficient and a prediction residual power that identify the power spectrum of a source signal, and noise parameter estimates, which include noise power spectrum estimates. Then, the maximum likelihood estimation is used to alternately repeat processing for updating at least one of the reverberation parameter estimates and the noise parameter estimates and processing for updating the source parameter estimates until a predetermined termination condition is satisfied.
摘要:
An inverse control system is disclosed, which comprises FIR filters provided between transmitting elements at n (n=2, 3, . . . ) input points of a linear FIR system and a common signal source, for an inverse control such as to provide desired impulse responses between the signal source and m (n>m) output points of the linear FIR system. A j-th (j=1, 2, . . . , n) one of the FIR filters has a number L.sub.j of taps which satisfies the relationships represented by ##EQU1## for all i=1, 2, . . . , m and j=1, 2 . . . , n where w.sub.ij is the number of discrete signals representing the impulse response g.sub.ij (k) between the j-th output point and i-output point and P.sub.i is the number of discrete signals representing the desired impulse response r.sub.i (k) between the signal source and i-th output point. The j-th FIR filters has a filter coefficient h.sub.j (k) satisfying the relationship ##EQU2## where .circle.* is a discrete convolution.
摘要:
Provided is a signal distortion elimination apparatus comprising: an inverse filter application means that outputs the signal obtained by applying an inverse filter to an observed signal as a restored signal when a predetermined iteration termination condition is met and outputs the signal obtained by applying the inverse filter to the observed signal as an ad-hoc signal when the predetermined iteration termination condition is not met; a prediction error filter calculation means that segments the ad-hoc signal into frames and outputs a prediction error filter of each frame obtained by performing linear prediction analysis of the ad-hoc signal of each frame; an inverse filter calculation means that calculates an inverse filter such that a concatenation of innovation estimates of the respective frames becomes mutually independent among their samples, where the innovation estimate of a single frame (an innovation estimate) is the signal obtained by applying the prediction error filter of the corresponding frame to the ad-hoc signal of the corresponding frame, and outputs the inverse filter; and a control means that iteratively executes the inverse filter application means, the prediction error filter calculation means and the inverse filter calculation means until the iteration termination condition is met.
摘要:
N correlated signals are processed by N pre-filters whose transfer characteristics have different zero points, then the processed signals are input into an N-input M-output linear FIR system, and its transfer characteristics are estimated from its response outputs and the processed signals from the pre-filters.
摘要:
Provided is a signal distortion elimination apparatus comprising: an inverse filter application means that outputs the signal obtained by applying an inverse filter to an observed signal as a restored signal when a predetermined iteration termination condition is met and outputs the signal obtained by applying the inverse filter to the observed signal as an ad-hoc signal when the predetermined iteration termination condition is not met; a prediction error filter calculation means that segments the ad-hoc signal into frames and outputs a prediction error filter of each frame obtained by performing linear prediction analysis of the ad-hoc signal of each frame; an inverse filter calculation means that calculates an inverse filter such that a concatenation of innovation estimates of the respective frames becomes mutually independent among their samples, where the innovation estimate of a single frame (an innovation estimate) is the signal obtained by applying the prediction error filter of the corresponding frame to the ad-hoc signal of the corresponding frame, and outputs the inverse filter; and a control means that iteratively executes the inverse filter application means, the prediction error filter calculation means and the inverse filter calculation means until the iteration termination condition is met.