Packet loss concealment for overlapped transform codecs

• US 20060209955A1
• Filed: 06/30/2005
• Published: 09/21/2006
• Est. Priority Date: 03/01/2005
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Chou, Philip A., Florencio, Dinei A.
• Granted Patent: US 7,627,467 B2
• Time in Patent Office: Days
• US Class Current: 375/240.120
• CPC Class Codes: G10L 19/005 Correction of errors induced by the transmission channel, if related to the coding algorithm
• CPC Class Codes: G10L 19/0212 using orthogonal transformation

Abstract

Real-time packet-based audio communications over packet-based networks frequently results in the loss of one or more packets during any given communication session. The real-time nature of such communications precludes retransmission of lost packets due to the unacceptable delays that would result. Consequently, packet loss concealment methods are employed to “hide” lost packets from the listener. Unfortunately, conventional loss concealment methods, such as packet repetition or stretch/overlap methods, do not fully exploit information available from partially received samples. Therefore, when a single frame of N coefficients is lost, 2N samples are only partially reconstructed, thereby degrading the reconstructed signal. To address this problem, an optimized packet loss concealment solution is identified for particular lost packets by solving an underdetermined system of linear equations representing partially received samples while minimizing a computed error based on a model of the signal obtained from neighboring blocks or frames received by the decoder.

Claims

1. A system for reconstructing blocks of samples of a signal corresponding to missing coefficients of a transform of the signal, comprising steps for:

extracting a set of coefficients from frames of the transform of the signal;

determining which coefficients are missing;

locating an under-determined block of samples of the signal corresponding to at least one missing coefficient;

constructing from a subset of the extracted coefficients a set of linear equations representing partial constraints on the under-determined block of samples;

modeling samples of the signal neighboring the under-determined Is block of samples to construct a basis for the under-determined block of samples;

optimizing the coefficients of the under-determined block of samples with respect to the constructed basis and the partial constraints; and

reconstructing a block of samples corresponding to the missing coefficients from the optimized coefficients with respect to the basis.

2. The system of claim 1, where the missing coefficients correspond to an entire frame of coefficients in an overlapped transform coded signal.

3. The system of claim 1 wherein modeling samples of the signal comprise steps for computing Linear Predictive Coding (LPC) filter coefficients for the neighboring samples, and wherein constructing the basis comprises steps for constructing a set of impulse responses of an LPC filter estimated from the computed LPC filter coefficients.

4. The system of claim 3 wherein the impulse responses are approximately periodic with a period approximately matching a pitch period estimated from the neighboring samples.

5. The system of claim 1 wherein optimizing the coefficients comprises steps for minimizing an energy of the coefficients with respect to the constructed basis and the partial constraints.

6. The system of claim 5 wherein minimizing the energy comprises steps for computing a pseudo-inverse with respect to the constructed basis and the partial constraints.

7. The system of claim 1 further comprising steps for maintaining a minimum signal buffer content during a real-time decoding and playback of frames from the signal buffer by using signal jitter control for any of stretching and compressing decoded signal frames.

8. A computer-readable medium having computer executable instructions for, reconstructing blocks of samples of a signal corresponding to missing frames of coefficients of an overlapped transform of the signal, said computer executable instructions comprising:

determining which frames are missing from a set of received frames of the overlapped transform of the signal;

locating an under-determined block of samples of the signal corresponding to a missing frame;

extracting coefficients from at least one received frame;

constructing from the extracted coefficients a set of linear equations representing partial constraints on the under-determined block of samples;

modeling samples of the signal neighboring the under-determined block of samples;

constructing from the modeled samples a basis for the under-determined block of samples;

optimizing the coefficients of the under-determined block of samples with respect to the constructed basis and the partial constraints; and

reconstructing a block of samples corresponding to the missing frame from the optimized coefficients with respect to the basis.

9. The computer-readable medium of claim 8 wherein modeling the samples is performed in the Linear Predictive Coding (LPC) domain by computing LPC filter coefficients for the neighboring samples, and the basis is constructed as a set of impulse responses of an LPC filter estimated from the computed LPC filter.

10. The computer-readable medium of claim 9 wherein the impulses are approximately periodic with period approximately matching a pitch period estimated from the neighboring samples.

11. The computer-readable medium of claim 8 wherein optimizing the coefficients comprises minimizing an energy of the coefficients with respect to the constructed basis and the partial constraints.

12. The computer-readable medium of claim 11 wherein minimizing the energy comprises performing a pseudo-inverse.

13. The computer-readable medium of claim 8 wherein the signal is an audio signal.

14. A method for reconstructing one or more missing data frames of an overlapped transform coded signal, comprising:

storing received data frames of the coded signal to a signal buffer;

determining whether any data frames are of the data frames are missing;

constructing a set of under-determined linear equations from partial information extracted from at least one of a preceding neighboring frame and a succeeding neighboring frame, relative to a missing frame;

modeling the at least one neighboring frame and using the at least one modeled neighboring frame for generating a basis for the missing frame;

identifying an optimal solution to the set of under-determined linear equations as a function of the generated basis;

reconstructing the missing frame from the identified optimal solution; and

inserting the reconstructed missing frame into its proper position between corresponding neighboring frames in the signal buffer.

15. The method of claim 14 wherein modeling the at least one neighboring frame further comprises modeling the at least one neighboring frames in the Linear Predictive Coding (LPC) domain by computing LPC filter coefficients for the neighboring frames.

16. The method of claim 15 wherein generating the basis for the missing frame further comprises extrapolating the at least one neighboring frames into the missing frame by obtaining no-excitation responses of the computed LPC filter coefficients to construct a set of basis functions for the missing frame from the LPC filter coefficients.

17. The method of claim 14 wherein identifying the optimal solution to the set of under-determined linear equations comprises choosing a linear equation that minimizes an energy error computed from the basis.

18. The method of claim 14 further comprising modifying the basis to approximately conform to an estimated pitch and periodicity computed from the at least one neighboring data frames.

19. The method of claim 18 wherein estimating the pitch and periodicity further comprises any of:

computing an average of the periodicity and pitch of the at least one neighboring data frames; and

computing a windowed decay of the pitch and periodicity from the preceding neighboring data frame into the missing data frame.

20. The method of claim 14, where boundary continuity between the reconstructed missing frame and the neighboring frames is assured by computing a signal extrapolation of at least one of the neighboring frames into the missing frame beforehand, and subtracting the influence of the signal extrapolation from the missing frame.