Spectrum modeling

A device (2), comprising:

• means (22) for determining filter parameters (pi,qi) of a filter (41) which has a frequency response (S′) approximating a target spectrum,
• characterized in that the device further comprises:
• means (22) for splitting the target spectrum (S) in at least a first part and a second part;
• means (22) for using a first modeling operation on the first part of the target spectrum (S) to obtain auto-regressive parameters (pi);
• means (22) for using a second modeling operation on the second part of the target spectrum (S) to obtain moving-average parameters (qi);
• and
• means (22) for combining the auto-regressive parameters (pi) and the moving-average parameters (qi) to obtain the filter parameters (pi,qi).

A method of suppressing noise (6) in an audio signal (A), the method comprising:

• modeling (60) a spectrum of the noise by determining filter parameters (pi,qi) of a filter (61) which has a frequency response approximating the spectrum of the noise;
• obtaining (61) reconstructed noise by filtering (61) a white noise (y) with a filter (61), which properties are determined by the filter parameters (pi,qi);
• and
• subtracting (62) the reconstructed noise from the audio signal (A) to obtain a noise-filtered audio signal ({A});
• the step of modeling (60) comprising:
• splitting (60) the spectrum in at least a first part and a second part;
• using (60) a first modeling operation on the first part of the spectrum to obtain auto-regressive parameters (pi);
• using (60) a second modeling operation on the second part of the noise spectrum to obtain moving-average parameters (qi);
• and
• combining (60) the auto-regressive parameters (pi) and the moving-average parameters (qi) to obtain the filter parameters (pi,qi);

A device (6) for suppressing noise in an audio signal (A), the device comprising:

• means (60) for modeling a spectrum of the noise by determining filter parameters (pi,qi) of a filter (61) which has a frequency response approximating the spectrum of the noise;
• means (61) for obtaining reconstructed noise by filtering (61) a white noise (y) with a filter (61), which properties are determined by the filter parameters (pi,qi);
• and
• means (62) for subtracting the reconstructed noise from the audio signal (A) to obtain a noise-filtered audio signal ({A});
• the means for modeling (60) comprising:
• means (60) for splitting the spectrum in at least a first part and a second part;
• means (60) for using a first modeling operation on the first part of the spectrum to obtain auto-regressive parameters (pi);
• means (60) for using a second modeling operation on the second part of the noise spectrum to obtain moving-average parameters (qi);
• and
• means (60) for combining the auto-regressive parameters (pi) and the moving-average parameters (qi) to obtain the filter parameters (pi,qi);

A method of encoding (2,21) an audio signal (A), comprising the steps of:

• determining (200) basic waveforms in the audio signal (A);
• obtaining (21) a noise component (S) from the audio signal (A) by subtracting the basic waveforms from the audio signal (A);
• modeling (22) a spectrum of the noise component (S) by determining filter parameters (pi,qi) of a filter (41) which has a frequency response (S′) approximating the spectrum of the noise component (S);
• and
• including (23) the filter parameters (pi,qi) and waveform parameters (Ci) representing the basic waveforms in an encoded audio signal (A′);
• the step of modeling comprising:
• splitting (22) the spectrum (S) in at least a first part and a second part;
• using (22) a first modeling operation on the first part of the spectrum (S) to obtain auto-regressive parameters (pi);
• using (22) a second modeling operation on the second part of the noise spectrum (S) to obtain moving-average parameters (qi);
• and
• combining (22) the auto-regressive parameters (pi) and the moving-average parameters (qi) to obtain the filter parameters (pi,qi).

An audio encoder (2) comprising:

• means (200) for determining basic waveforms in the audio signal (A);
• means (21) for obtaining a noise component (S) from the audio signal (A) by subtracting (21) the basic waveforms from the audio signal (A);
• means (22) for modeling a spectrum of the noise component (S) by determining filter parameters (pi,qi) of a filter (41) which has a frequency response (S′) approximating the spectrum of the noise component (S);
• and
• means (23) for including the filter parameters (pi,qi) and waveform parameters (Ci) representing the basic waveforms in an encoded audio signal (A′);
• the means (22) for modeling comprising:
• means (22) for splitting the spectrum (S) in at least a first part and a second part;
• means (22) for using a first modeling operation on the first part of the spectrum (S) to obtain auto-regressive parameters (pi);
• means (22) for using a second modeling operation on the second part of the noise spectrum (S) to obtain moving-average parameters (qi);
• and
• means (22) for combining the auto-regressive parameters (pi) and the moving-average parameters (qi) to obtain the filter parameters (pi,qi).