Signal processing apparatus, signal processing method, and program

A signal processing device comprising processing circuitry, the processing circuitry comprising:

• an estimation unit that estimates a convolutional beamformer, wherein the convolutional beamformer is used for calculating, at each time point, a weighted sum of a current signal and a past signal of a sequence of past signals with a predetermined delay and a time duration of the sequence of past signals of zero length or more, and the estimating the convolutional beamformer further comprises:
• receiving frequency-divided observation signals obtained from acoustic signals emitted from a target sound source;
• and
• determining, at each time point of the sequence of time points, weight of the weighted sum as the convolutional beamformer, wherein the weighted sum causes estimation signals of target signals to increase a probability of speech-likeliness of the estimation signals based on a predetermined probability model;
• and
• a suppression unit that suppresses noise and reverberation associated with the frequency-divided observation signals to generate the estimation signals of the target signals by using the convolutional beamformer upon the frequency-divided observation signals, wherein
• the estimation unit acquires the convolutional beamformer which minimizes a sum of values acquired by weighting power of the estimation signals at respective time points belonging to a predetermined time interval by reciprocals of the power of the target signals or reciprocals of an estimated power of the target signals, under a constraint condition in which the target signals are not distorted as a result of applying the convolutional beamformer to the frequency-divided observation signals where the target signals are signals that correspond to a direct sound and an initial reflected sound within signals corresponding to a sound emitted from the target sound source and picked up by a microphone.

A signal processing method comprising:

• an estimation step of estimating a convolutional beamformer, wherein the convolutional beamformer is used for calculating, at each time point, a weighted sum of a current signal and a past signal of a sequence of past signals with a predetermined delay and a time duration of the sequence of past signals of zero length or more, and the estimating the convolutional beamformer further comprises:
• receiving frequency-divided observation signals obtained from acoustic signals emitted from a target sound source;
• calculating, at each time point of the sequence of time points, weights of the weighted sum as the convolutional beamformer, wherein the weighted sum causes the estimation signals of the target signals to increase a probability of speech-likeliness of the estimation signals based on a predetermined probability model;
• and
• a suppression step of suppressing noise and reverberation associated with the frequency-divided observation signals to generate the estimation signals of the target signals by using the convolutional beamformer upon the frequency-divided observation signals, wherein
• the probability expressing the speech-likeness is according to a signal distribution of speech in the estimation signals of the target signals, and an average of the estimation signals is 0 and a variance of the estimation signals varies over time.

A signal processing method comprising:

• an estimation step of estimating a convolutional beamformer, wherein the convolutional beamformer is used for calculating, at each time point, a weighted sum of a current signal and a past signal of a sequence of past signals with a predetermined delay and a time duration of the sequence of past signals of zero length or more, and the estimating the convolutional beamformer further comprises:
• receiving frequency-divided observation signals obtained from acoustic signals emitted from a target sound source;
• and
• determining, at each time point of the sequence of time points, weights of the weighted sum as the convolutional beamformer, wherein the weighted sum causes the estimation signals of the target signals to increase a probability of speech-likeliness of the estimation signals based on a predetermined probability model;
• and
• a suppression step of suppressing noise and reverberation associated with the frequency-divided observation signals to generate the estimation signals of the target signals by using the convolutional beamformer upon the frequency-divided observation signals, wherein
• the estimation step acquires the convolutional beamformer which minimizes a sum of values acquired by weighting power of the estimation signals at respective time points belonging to a predetermined time interval by reciprocals of the power of the target signals or reciprocals of an estimated power of the target signals, under a constraint condition in which the target signals are not distorted as a result of applying the convolutional beamformer to the frequency-divided observation signals where the target signals are signals that correspond to a direct sound and an initial reflected sound within signals corresponding to a sound emitted from the target sound source and picked up by a microphone.