Abstract:
A signal that includes noise (301) is sampled to provide a plurality of digital information samples (303). A predetermined number of the digital information samples are grouped as a set (305). Noise suppression is performed on the signal using the following steps. One or more digital representations of silence is attached to the set, forming an extended set (401). A Fourier transform is performed on the extended set, yielding a set of frequency domain coefficients (403), at least some of which are scaled (405). An inverse Fourier transform is performed on the set of scaled frequency domain coefficients to provide a set of time domain samples (407), which are partially overlapped in time and added with a previously formed set of time domain samples (409 and 411), which result is provided with the non-overlapping time domain samples as a noise suppressed version of the signal (413).
Abstract:
A signal that includes noise is sampled to provide a plurality of digital information samples. A predetermined number of the digital information samples are grouped as a set. Noise suppression is performed on the signal using the following steps. One or more digital representations of silence is attached to the set, forming an extended set (401). A Fourier transform is performed on the extended set, yielding a set of frequency domain coefficients (403), at least some of which are scaled (405). An inverse Fourier transform is performed on the set of scaled frequency domain coefficients to provide a set of time domain samples (407), which are partially overlapped in time and added with a previously formed set of time domain samples (409 and 411), which result is provided with the non-overlapping time domain samples as a noise suppressed version of the signal (413).
Abstract:
A system or method for modeling a signal, such as a speech signal, in which harmonic frequencies and amplitudes are identified and the harmonic magnitudes are interpolated to obtain spectral magnitudes at a set of fixed frequencies. An inverse transform is applied to the spectral magnitudes to obtain a pseudo auto-correlation sequence, from which linear prediction coefficients are calculated. From the linear prediction coefficients, model harmonic magnitudes are generated by sampling the spectral envelope defined by the linear prediction coefficients. A set of scale factors are then calculated as the ratio of the harmonic magnitudes to the model harmonic magnitudes and interpolated to obtain a second set of scale factors at the set of fixed frequencies. The spectral envelope magnitudes at the set of fixed frequencies are multiplied by the second set of scale factors to obtain new spectral magnitudes and the process is iterated to obtain final linear prediction coefficients. The signal is modeled by the linear prediction coefficients.
Abstract:
A method and apparatus for speech reconstruction within a distributed speech recognition system is provided herein. Missing MFCCs are reconstructed and utilized to generate speech. Particularly, partial recovery of the missing MFCCs is achieved by exploiting the dependence of the missing MFCCs on the transmitted pitch period P as well as on the transmitted MFCCs. Harmonic magnitudes are then obtained from the transmitted and reconstructed MFCCs, and the speech is reconstructed utilizing these harmonic magnitudes.
Abstract:
A system or method for modeling a signal, such as a speech signal, in which harmonic frequencies and amplitudes are identified and the harmonic magnitudes are interpolated to obtain spectral magnitudes at a set of fixed frequencies. An inverse transform is applied to the spectral magnitudes to obtain a pseudo auto-correlation sequence, from which linear prediction coefficients are calculated. From the linear prediction coefficients, model harmonic magnitudes are generated by sampling the spectral envelope defined by the linear prediction coefficients. A set of scale factors are then calculated as the ratio of the harmonic magnitudes to the model harmonic magnitudes and interpolated to obtain a second set of scale factors at the set of fixed frequencies. The spectral envelope magnitudes at the set of fixed frequencies are multiplied by the second set of scale factors to obtain new spectral magnitudes and the process is iterated to obtain final linear prediction coefficients. The signal is modeled by the linear prediction coefficients.
Abstract:
A method and apparatus for suppressing acoustic background noise in a communication system. An operating signal-to-noise ratio (SNR) level is reliably evaluated from channel energy (293) and background noise energy (294) values by a SNR level estimator (295). A minimum gain factor and a gain slope are adapted (290) depending on the operating SNR level. Using these adapted values and the channel SNR, the channel gain is selected (233). When the channel SNR is below a certain threshold, the channel is completely noise-like and the gain factor selected is minimum so that the channel is maximally attenuated. When the channel SNR is fairly high, the channel gain selected is 0 dB. For intermediate values of channel SNR, the gain factor selected lies between minimum and 0 dB.
Abstract:
A signal that includes noise (301) is sampled to provide a plurality of digital information samples (303). A predetermined number of the digital information samples are grouped as a set (305). Noise suppression is performed on the signal using the following steps. One or more digital representations of silence is attached to the set, forming an extended set (401). A Fourier transform is performed on the extended set, yielding a set of frequency domain coefficients (403), at least some of which are scaled (405). An inverse Fourier transform is performed on the set of scaled frequency domain coefficients to provide a set of time domain samples (407), which are partially overlapped in time and added with a previously formed set of time domain samples (409 and 411), which result is provided with the non-overlapping time domain samples as a noise suppressed version of the signal (413).