Signal classifying method and apparatus

• US 8,050,916 B2
• Filed: 04/12/2011
• Issued: 11/01/2011
• Est. Priority Date: 10/15/2009
• Status: Active Grant

• Current Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: Huawei Technologies Co., Ltd. (Huawei Investment & Holding Co., Ltd.)
• Inventors: Shlomot, Eyal, Liu, Yuanyuan, Wang, Zhe
• Primary Examiner(s): ALBERTALLI, BRIAN LOUIS
• Application Number:
  US13/085,149
• Publication Number:
  US 20110178796A1
• Time in Patent Office: 203 Days
• Field of Search: None
• US Class Current: 704/233
• CPC Class Codes: G10L 2025/786 Adaptive threshold
• CPC Class Codes: G10L 25/81 for discriminating voice from music

Abstract

A signal classifying method and apparatus are disclosed. The signal classifying method includes: obtaining a spectrum fluctuation parameter of a current signal frame determined as a foreground frame, and buffering the spectrum fluctuation parameter; obtaining a spectrum fluctuation variance of the current signal frame according to spectrum fluctuation parameters of all buffered signal frames, and buffering the spectrum fluctuation variance; and calculating a ratio of signal frames whose spectrum fluctuation variance is above or equal to a first threshold to all the buffered signal frames, and determining the current signal frame as a speech frame if the ratio is above or equal to a second threshold or determining the current signal frame as a music frame if the ratio is below the second threshold. In the embodiments of the present invention, the spectrum fluctuation variance of the signal is used as a parameter for classifying the signals, and a local statistical method is applied to decide the type of the signal. Therefore, the signals are classified with few parameters, simple logical relations and low complexity.

Claims

1. A signal classifying method, comprising:

obtaining a spectrum fluctuation parameter of a current signal frame;

buffering the spectrum fluctuation parameter of the current signal frame in a first buffer array if the current signal frame is a foreground frame;

if the current signal frame falls within a first number of initial signal frames, setting a spectrum fluctuation variance of the current signal frame to a specific value and buffering the spectrum fluctuation variance of the current signal frame in a second buffer array;

otherwise, obtaining the spectrum fluctuation variance of the current signal frame according to spectrum fluctuation parameters of all signal frames buffered in the first buffer array and buffering the spectrum fluctuation variance of the current signal frame in the second buffer array; and

calculating a ratio of signal frames whose spectrum fluctuation variance is above or equal to a first threshold to all signal frames buffered in the second buffer array, and determining the current signal frame as a speech frame if the ratio is above or equal to a second threshold or determining the current signal frame as a music frame if the ratio is below the second threshold.

2. The signal classifying method according to claim 1, wherein the first threshold is a first adaptive threshold, and the first adaptive threshold is obtained according to a Modified Segmental Signal Noise Ratio (MSSNR) or a Signal-to-Noise Ratio (SNR).

3. The signal classifying method according to claim 2, wherein obtaining the first adaptive threshold according to the MSSNR comprises:

updating a maximal value of the MSSNR according to the current signal frame;

determining a threshold of the MSSNR according to the updated maximal value of the MSSNR;

obtaining the number of frames whose MSSNR is above the MSSNR threshold and number of frames whose MSSNR is below or equal to the MSSNR threshold among a certain number of frames inclusive of the current signal frame;

calculating a difference measure between the number of frames whose MSSNR is above the MSSNR threshold and the number of frames whose MSSNR is below or equal to the MSSNR threshold; and

obtaining the first adaptive threshold according to the difference measure.

4. The signal classifying method according to claim 2, wherein obtaining the first adaptive threshold according to the SNR comprises:

updating a maximal value of the SNR according to the current signal frame;

determining a threshold of the SNR according to the updated maximal value of the SNR;

obtaining the number of frames whose SNR is above the SNR threshold and number of frames whose SNR is below or equal to the SNR threshold among a certain number of frames inclusive of the current signal frame;

calculating a difference measure between the number of frames whose SNR is above the SNR threshold and the number of frames whose SNR is below or equal to the SNR threshold; and

obtaining the first adaptive threshold according to the difference measure.

5. The signal classifying method according to claim 1, wherein the method further comprises using other parameters in addition to the spectrum fluctuation variance as a basis for assisting in classifying the signals, which comprises:

making an auxiliary decision according to a first peakiness measure and/or a second peakiness measure.

6. The signal classifying method according to claim 1, wherein after obtaining a decision result which indicates that the current signal frame is a speech frame or a music frame, the method further comprises:

applying a hangover of a frame to the decision result to obtain a final decision result.

7. The signal classifying method according to claim 1, wherein the method of determining the current signal frame as a foreground frame comprises:

using the MSSNR or the SNR as a basis of the decision; and

determining the current signal frame as a foreground frame if the MSSNR is above or equal to a third threshold or the SNR is above or equal to a fourth threshold.

8. The signal classifying method according to claim 1, wherein before obtaining the ratio of signal frames whose spectrum fluctuation variance is above or equal to the first threshold to all the signal frames buffered in the second buffer array, the method further comprises:

performing windowed smoothing for several initial spectrum fluctuation variance values buffered in the second buffer array.

9. A signal classifying apparatus, comprising:

a first obtaining module, configured to obtain a spectrum fluctuation parameter of a current signal frame;

a foreground frame determining module, configured to determine the current signal frame as a foreground frame and buffer the spectrum fluctuation parameter of the current signal frame determined as the foreground frame into a first buffering module;

the first buffering module, configured to buffer the spectrum fluctuation parameter of the current signal frame determined by the foreground frame determining module;

a setting module, configured to set a spectrum fluctuation variance of the current signal frame to a specific value and buffer the spectrum fluctuation variance in a second buffering module if the current signal frame falls within a first number of initial signal frames;

a second obtaining module, configured to obtain the spectrum fluctuation variance of the current signal frame according to spectrum fluctuation parameters of all signal frames buffered in the first buffering module and buffer the spectrum fluctuation variance of the current signal frame in the second buffering module if the current signal frame falls outside the first number of initial signal frames;

the second buffering module, configured to buffer the spectrum fluctuation variance of the current signal frame set by the setting module or obtained by the second obtaining module; and

a first deciding module, configured to;

calculate a ratio of signal frames whose spectrum fluctuation variance is above or equal to a first threshold to all signal frames buffered in the second buffering module, and determine the current signal frame as a speech frame if the ratio is above or equal to a second threshold or determine the current signal frame as a music frame if the ratio is below the second threshold.

10. The signal classifying apparatus according to claim 9, wherein the first deciding module comprises:

a first threshold determining unit, configured to determine the first threshold;

a ratio obtaining unit, configured to obtain the ratio of the signal frames whose spectrum fluctuation variance is above or equal to the first threshold determined by the first threshold determining unit to all the signal frames buffered in the second buffering module;

a second threshold determining unit, configured to determine the second threshold; and

a judging unit, configured to;

compare the ratio obtained by the ratio obtaining unit with the second threshold determined by the second threshold determining unit; and

determine the current signal frame as a speech frame if the ratio is above or equal to the second threshold, or determine the current signal frame as a music frame if the ratio is below the second threshold.

11. The signal classifying apparatus according to claim 9, further comprising:

a second deciding module, configured to assist the first deciding module in classifying the signals according to other parameters.

12. The signal classifying apparatus according to claim 9, further comprising:

a decision correcting module, configured to obtain a final decision result by applying a hangover of a frame to the decision result obtained by the first deciding module or obtained by both the first deciding module and the second deciding module, wherein the decision result indicates whether the current signal frame is a speech frame or a music frame.

13. The signal classifying apparatus according to claim 9, further comprising:

a windowing module, configured to;

perform windowed smoothing for several initial spectrum fluctuation variance values buffered in the second buffering module before the first deciding module calculates the ratio of the signal frames whose spectrum fluctuation variance is above or equal to the first threshold to all the signal frames buffered in the second buffering module.