US5911170A
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Synthesis of acoustic waveforms based on parametric modeling
Assignee: Texas Instruments Inc
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Filing: 1998-02-27
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Granted: 1999-06-08
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Expires: 2018-02-27
Abstract
Abstract A method is disclosed for synthesizing acoustic waveforms, especially musical instrument sounds. The acoustic waveforms are characterized by time-varying amplitudes, frequencies and phases of sinusoidal components. These time-varying parameters, at each analysis frame, are obtained in one embodiment by short term Fourier transforms (STFT). The spectrum envelope at each frame is parameterized with an autoregressive moving average model and applied to a waveform consisting of unit amplitude sinusoids via time-domain filtering. The resulting synthetic waveform preserves the time-varying frequency and phase information and has the same relative energy distribution among different sinusoidal components as that of the original signal. Finally, a general waveform shape for the type of acoustic signal being synthesized is applied. This is particularly useful when musical instrument sounds are being synthesized, where the commonly used four piecewise-linear attack-decay-sustain-release (ADSR) envelope model can be employed.
Inventors
- 👤 Yinong Ding
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 19:59
CPC Classifications
G; G10; G10H; G10H7/00; G10H7/08; G10H7/10; G10H7/105; G10H1/00; G10H1/02; G10H1/06; G10H1/12; G10H1/125; G10H2250/00; G10H2250/055; G10H2250/081; G10H2250/131; G10H2250/215; G10H2250/235; Y; Y10; Y10S; Y10S84/00; Y10S84/09
Claims Analysis
Total Claims: 10
Claim 1
Independent Claim
112 words
A method of synthesizing an acoustic waveform modeled as a sum of sinusoids with time-varying amplitudes and frequencies, comprising: generating a flat spectrum signal comprising a sum of constant amplitude sinusoids with time-varying frequencies using a cubic phase interpolation algorithm with frequency parameter inputs f k (t) derived from DFT-based analysis of sampled waveform data; generating a weighted spectrum signal comprising a sum of time-varying relative magnitudes of different frequency components by filtering the flat spectrum signal using an autoregressive moving average (ARMA) filter whose inputs B(t), A(t) are derived from spectrum envelope shape analysis of the sampled waveform data; and applying an overall time-varying amplitude envelope to the weighted spectrum signal.
Claim 2
Independent Claim
112 words
A method of synthesizing an acoustic waveform modeled as a sum of sinusoids with time-varying amplitudes and frequencies, comprising: generating a flat spectrum signal comprising a sum of constant amplitude sinusoids with time-varying frequencies using a cubic phase interpolation algorithm with frequency parameter inputs f k (t) derived from DFT-based analysis of sampled waveform data; generating a weighted spectrum signal comprising a sum of time-varying relative magnitudes of different frequency components by filtering the flat spectrum signal using an autoregressive moving average (ARMA) filter whose inputs B(t), A(t) are derived from spectrum envelope shape analysis of the sampled waveform data; and applying an overall time-varying amplitude envelope to the weighted spectrum signal.
Claim 3
Dependent Claim
20 words
The method of claim 1, wherein the flat signal spectrum generating step comprises generating a sum of unit amplitude sinusoids.
Claim 4
Dependent Claim
18 words
The method of claim 1, wherein the overall time-varying amplitude envelope is a four piecewise linear attack-decay-sustain-release model.
Claim 5
Dependent Claim
45 words
The method of claim 1, wherein the frequency parameter inputs f k (t) are derived from the DFT maximal likelihood estimates obtained from a sequence frames of 256 data samples each obtained from sampling a musical instrument sound waveform at a sampling rate of 44.1kHz.
Claim 6
Dependent Claim
52 words
The method of claim 1, wherein the filter inputs B(t), A(t) are derived from linear interpolation, homomorphic transformation and ARMA model fitting using amplitude parameter inputs a k (t) derived by least-squares fitting of the sampled waveform data using a form model matrix derived from the frequency parameter inputs f k (t).
Claim 7
Independent Claim
149 words
A method of synthesizing an acoustic waveform modeled as a sum of sinusoids with time-varying amplitudes and frequencies, comprising: generating a flat spectrum signal comprising a sum of constant amplitude sinusoids with time-varying frequencies using a cubic phase interpolation algorithm with frequency parameter inputs f k (t) derived from DFT maximal likelihood estimates of a sampled musical instrument sound waveform; generating a weighted spectrum signal comprising a sum of time-varying relative magnitudes of different frequency components by filtering the flat spectrum signal using an autoregressive moving average (ARMA) filter whose inputs B(t), A(t) are derived from linear interpolation, homomorphic transformation and ARMA model fitting using amplitude parameter inputs a k (t) derived by least-squares fitting of the sampled waveform data using a form model matrix derived from the frequency parameter inputs f k (t); and applying piecewise linear attack-decay-sustain-release overall time-varying amplitude model envelope to the weighted spectrum signal.
Claim 8
Independent Claim
149 words
A method of synthesizing an acoustic waveform modeled as a sum of sinusoids with time-varying amplitudes and frequencies, comprising: generating a flat spectrum signal comprising a sum of constant amplitude sinusoids with time-varying frequencies using a cubic phase interpolation algorithm with frequency parameter inputs f k (t) derived from DFT maximal likelihood estimates of a sampled musical instrument sound waveform; generating a weighted spectrum signal comprising a sum of time-varying relative magnitudes of different frequency components by filtering the flat spectrum signal using an autoregressive moving average (ARMA) filter whose inputs B(t), A(t) are derived from linear interpolation, homomorphic transformation and ARMA model fitting using amplitude parameter inputs a k (t) derived by least-squares fitting of the sampled waveform data using a form model matrix derived from the frequency parameter inputs f k (t); and applying piecewise linear attack-decay-sustain-release overall time-varying amplitude model envelope to the weighted spectrum signal.
Claim 9
Dependent Claim
20 words
The method of claim 6, wherein the flat signal spectrum generating step comprises generating a sum of unit amplitude sinusoids.
Claim 10
Dependent Claim
45 words
The method of claim 7, wherein the frequency parameter inputs f k (t) are derived from the DFT maximal likelihood estimates obtained from a sequence frames of 256 data samples each obtained from sampling a musical instrument sound waveform at a sampling rate of 44.1kHz.
References Cited (Backward Citations)
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| 3.5 Years (Year 4) | 2002-06-07 | 2002-12-07 | 2003-06-08 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2006-06-07 | 2006-12-07 | 2007-06-08 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2010-06-07 | 2010-12-07 | 2011-06-08 | $7700.00 | Window Closed / Lapsed |