Linear prediction residual energy tilt-based audio signal classification method and apparatus

Patent metadata

Field	Value
Publication number	US-11289113-B2
Application number	US-201916723584-A
Country	US
Kind code	B2
Filing date	Dec 20, 2019
Priority date	Aug 6, 2013
Publication date	Mar 29, 2022
Grant date	Mar 29, 2022

Abstract

A linear prediction residual energy tilt-based audio signal classification method and apparatus, where the method includes: determining, according to voice activity of a current audio frame, whether to obtain a linear prediction residual energy tilt of a current audio frame of the current audio frame and store a frequency spectrum fluctuation of the current frame in a frequency spectrum fluctuation memory, where the linear prediction residual energy tilt denotes an extent to which an audio signal's linear prediction residual energy changes as a linear prediction order inscreases; updating, according to whether the audio frame is percussive music or activity of a historical audio frame, frequency spectrum fluctuations stored in the frequency spectrum fluctuation memory; and classifying the current audio frame as a speech frame or a music frame according to statistics of some or all of effective data of the frequency spectrum fluctuations stored in the frequency spectrum fluctuation memory.

First claim

What is claimed is:

1. An audio signal classification method, comprising:
performing frame division processing on an input audio signal;
obtaining a linear prediction residual energy tilt of a current audio frame of the input audio signal, wherein the linear prediction residual energy tilt denotes an extent to which linear prediction residual energy of the input audio signal changes as a linear prediction order increases;
determining whether to store the linear prediction residual energy tilt in a memory according to voice activity of the current audio frame;
storing the linear prediction residual energy tilt in the memory in response to determining that the linear prediction residual energy tilt needs to be stored according to the voice activity of the current audio frame; and
classifying the current audio frame according to statistics of prediction residual energy tilts in the memory.

2. The audio signal classification method according to claim 1 , wherein the statistics of the prediction residual energy tilts is a variance of the prediction residual energy tilts, and wherein classifying the current audio frame according to the statistics of the prediction residual energy tilts in the memory comprises:
comparing the variance of the prediction residual energy tilts with a music classification threshold; and
classifying the current audio frame as a music frame when the variance of the prediction residual energy tilts is less than the music classification threshold.

3. The audio signal classification method according to claim 1 , wherein the statistics of the prediction residual energy tilts is a variance of the prediction residual energy tilts, and wherein classifying the current audio frame according to the statistics of the prediction residual energy tilts in the memory comprises:
comparing the variance of the prediction residual energy tilts with a music classification threshold; and
classifying the current audio frame as a speech frame when the variance of the prediction residual energy tilts is greater than or equal to the music classification threshold.

4. The audio signal classification method according to claim 1 , further comprising:
obtaining a frequency spectrum fluctuation, a frequency spectrum high-frequency-band peakiness, and a frequency spectrum correlation degree of the current audio frame; and
storing the frequency spectrum fluctuation, the frequency spectrum high-frequency-band peakiness, and the frequency spectrum correlation degree in corresponding memories,
wherein classifying the current audio frame according to the statistics of the prediction residual energy tilts in the memory comprises:
obtaining statistics of effective data of the frequency spectrum fluctuation, statistics of effective data of the frequency spectrum high-frequency-band peakiness, statistics of effective data of the frequency spectrum correlation degree, and statistics of effective data of the linear prediction residual energy tilt; and
classifying the current audio frame as a speech frame or a music frame according to statistics of effective data, wherein each statistics of the effective data is a data value.

5. The audio signal classification method according to claim 4 , wherein the obtaining the statistics of the effective data of the frequency spectrum fluctuation, the statistics of the effective data of the frequency spectrum high-frequency-band peakiness, the statistics of the effective data of the frequency spectrum correlation degree, and the statistics of the effective data of the linear prediction residual energy tilt, and classifying the audio current frame as a speech frame or a music frame according to the statistics of the effective data comprises:
obtaining an average value of the effective data of the frequency spectrum fluctuation, an average value of the effective data of the frequency spectrum high-frequency-band peakiness, an average value of the effective data of the frequency spectrum correlation degree, and a variance of the effective data of the linear prediction residual energy tilt separately; and
classifying the current audio frame as the music frame when one of the following conditions is satisfied:
the average value of the effective data of the frequency spectrum fluctuation is less than a first threshold,
the average value of the effective data of the frequency spectrum high-frequency-band peakiness is greater than a second threshold,
the average value of the effective data of the frequency spectrum correlation degree is greater than a third threshold, and
the variance of the effective data of the linear prediction residual energy tilt is less than a fourth threshold.

6. The audio signal classification method according to claim 4 , wherein the obtaining the statistics of the effective data of the frequency spectrum fluctuation, the statistics of the effective data of the frequency spectrum high-frequency-band peakiness, the statistics of the effective data of the frequency spectrum correlation degree, and the statistics of the effective data of the linear prediction residual energy tilt, and classifying the audio current frame as a speech frame or a music frame according to the statistics of the effective data comprises:
obtaining an average value of the effective data of the frequency spectrum fluctuation, an average value of the effective data of the frequency spectrum high-frequency-band peakiness, an average value of the effective data of the frequency spectrum correlation degree, and a variance of the effective data of the linear prediction residual energy tilt separately; and
classifying the current audio frame as the speech frame when none of the following conditions are satisfied:
the average value of the effective data of the frequency spectrum fluctuation is less than a first threshold, the average value of the effective data of the frequency spectrum high-frequency-band peakiness is greater than a second threshold,
the average value of the effective data of the frequency spectrum correlation degree is greater than a third threshold, and
the variance of the effective data of the linear prediction residual energy tilt is less than a fourth threshold.

7. The audio signal classification method according to claim 1 , further comprising:
obtaining a frequency spectrum tone quantity of the current audio frame and a ratio of the frequency spectrum tone quantity on a low frequency band; and
storing the frequency spectrum tone quantity and the ratio of the frequency spectrum tone quantity on the low frequency band in corresponding memories,
wherein the classifying the current audio frame according to the statistics of the prediction residual energy tilts in the memory comprises:
obtaining statistics of the linear prediction residual energy tilt and statistics of the frequency spectrum tone quantity separately; and
classifying the current audio frame as a speech frame or a music frame according to the statistics of the linear prediction residual energy tilt, the statistics of the frequency spectrum tone quantity, and the ratio of the frequency spectrum tone quantity on the low frequency band, wherein each of the statistics refers to a data value obtained after a calculation operation is performed on data stored in the memories.

8. The audio signal classification method according to claim 7 , wherein obtaining the statistics of the linear prediction residual energy tilt and the statistics of the frequency spectrum tone quantity separately comprises:
obtaining a variance of the linear prediction residual energy tilt; and
obtaining an average value of the frequency spectrum tone quantity, and
wherein classifying the current audio frame as the speech frame or music frame according to the data val

Assignees

• Huawei Tech Co Ltd
• Huawei Technolgies Co Ltd

Inventors

• Wang Zhe

Classifications

• G10L25/81 Primary

  for discriminating voice from music · CPC title

• G10L25/78

  Detection of presence or absence of voice signals (switching of direction of transmission by voice frequency in two-way loud-speaking telephone systems H04M9/10) · CPC title

• G10L25/12

  the extracted parameters being prediction coefficients · CPC title

• G10L19/06

  Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients · CPC title

• G10L25/18 Primary

  the extracted parameters being spectral information of each sub-band · CPC title