Noisy speech autoregression parameter enhancement method and apparatus
First Claim
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1. A noisy speech parameter enhancement method, comprising the steps ofreceiving background noise samples and noisy speech samples;
- determining a background noise power spectral density estimate at M frequencies, where M is a predetermined positive integer, from a first collection of background noise samples;
estimating p autoregressive parameters, where p is a predetermined positive integer significantly smaller than M, and a first residual variance from a second collection of noisy speech samples;
determining a noisy speech power spectral density estimate at said M frequencies from said p autoregressive parameters and said first residual variance;
determining an enhanced speech power spectral density estimate by subtracting said background noise spectral density estimate multiplied by a predetermined positive factor from said noisy speech power spectral density estimate; and
determining r enhanced autoregressive parameters using an iterative algorithm, where r is a predetermined positive integer, and an enhanced residual variance from said enhanced speech power spectral density estimate using an iterative algorithm.
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Abstract
Noisy speech parameters are enhanced by determining a background noise power spectral density (PSD) estimate, determining noisy speech parameters, determining a noisy speech PSD estimate from the speech parameters, subtracting a background noise PSD estimate from the noisy speech PSD estimate, and estimating enhanced speech parameters from the enhanced speech PSD estimate.
173 Citations
20 Claims
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1. A noisy speech parameter enhancement method, comprising the steps of
receiving background noise samples and noisy speech samples; -
determining a background noise power spectral density estimate at M frequencies, where M is a predetermined positive integer, from a first collection of background noise samples;
estimating p autoregressive parameters, where p is a predetermined positive integer significantly smaller than M, and a first residual variance from a second collection of noisy speech samples;
determining a noisy speech power spectral density estimate at said M frequencies from said p autoregressive parameters and said first residual variance;
determining an enhanced speech power spectral density estimate by subtracting said background noise spectral density estimate multiplied by a predetermined positive factor from said noisy speech power spectral density estimate; and
determining r enhanced autoregressive parameters using an iterative algorithm, where r is a predetermined positive integer, and an enhanced residual variance from said enhanced speech power spectral density estimate using an iterative algorithm. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
estimating q autoregressive parameters, where q is a predetermined positive integer smaller than p, and a second residual variance from said first collection of background noise samples; determining said background noise power spectral density estimate at said M frequencies from said q autoregressive parameters and said second residual variance.
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7. The method of claim 6, including the step of averaging said background noise power spectral density estimate over a predetermined number of collections of background noise samples.
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8. The method of claim 1 including the step of averaging said background noise power spectral density estimate over a predetermined number of collections of background noise samples.
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9. The method of claim 1, including the step of using said enhanced autoregressive parameters and said enhanced residual variance for adjusting a filter for filtering a third collection of noisy speech samples.
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10. The method of claim 9, wherein said second and said third collection of noisy speech samples are formed by the same collection.
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11. The method of claim 10, including the step of Kalman filtering said third collection of noisy speech samples.
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12. The method of claim 9, including the step of Kalman filtering said third collection of noisy speech samples.
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13. A noisy speech parameter enhancement apparatus, comprising
means for receiving background noise samples and noisy speech samples; -
means for determining a background noise power spectral density estimate at M frequencies, where M is a predetermined positive integer, from a first collection of background noise samples;
means for estimating p autoregressive parameters, where p is a predetermined positive integer significantly smaller the M, and a first residual variance from a second collection of noisy speech samples;
means for determining a noisy speech power spectral density estimate at said M frequencies from said p autoregressive parameters and said first residual variance;
means for determining an enhanced speech power spectral density estimate by subtracting said background noise spectral density estimate multiplied by a predetermined factor from said noisy speech power spectral density estimate using an iterative algorithm; and
means for determining r enhanced autoregressive parameters using an iterative algorithm, where r is a predetermined positive integer, and an enhanced residual variance from said enhanced speech power spectral density. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
means for estimating q autoregressive parameters, where q is a predetermined positive integer smaller than p, and a second residual variance from said first collection of background noise samples; means for determining said background noise power spectral density estimate at said M frequencies from said q autoregressive parameters and said second residual variance.
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16. The apparatus of claim 15, including means for averaging said background noise power spectral density estimate over a predetermined number of collections of background noise samples.
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17. The apparatus of claim 13, including means for averaging said background noise power spectral density estimate over a predetermined number of collections of background noise samples.
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18. The apparatus of claim 13, including means for using said enhanced autoregressive parameters and said enhanced residual variance for adjusting a filter for filtering a third collection of noisy speech samples.
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19. The apparatus of claim 18, including a Kalman filter for filtering said third collection of noisy speech samples.
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20. The apparatus of claim 18, including a Kalman filter for filtering said third collection of noisy speech samples, said second and said third collection of noisy speech samples being being the same collection.
Specification