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Problems solved by technology
[0004] In the process of realizing the invention, the inventor found that: the method of signal classification using the decision tree needs to calculate more parameters and logical branches, and the complexity is higher
Method used
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Embodiment approach 1
[0081] Embodiment one, the method for determining the foreground frame using MSSNR:
[0082] Obtain the MSSNRn of the current signal frame, and when MSSNRn≥alpha1, determine that the current signal frame is a foreground frame, otherwise it is a background frame. Wherein, MSSNRn represents the corrected subband SNR sum of the nth frame, and alpha1 is a set threshold value. For clarity of description, the threshold value alpha1 is referred to as the third threshold value in the embodiment of the present invention, and the value of alpha1 can be any value , for example, alpha1=50 may be set in some embodiments.
[0083] In the embodiment of the present invention, MSSNRn can be obtained in a variety of ways, and in some implementations, it can be obtained in the following ways:
[0084] 1. Calculate the spectrum subband energy E of the current signal frame i .
[0085] Divide the spectrum into w subbands, 0≤w≤N 1 , and the energy of each subband is denoted as E i , i=0,1,2......
Embodiment approach 2
[0095] Embodiment 2, the method of using snr to determine the foreground frame:
[0096] Get the snr of the current signal frame n , when snr n When ≥alpha2, it is determined that the current signal frame is a foreground frame, otherwise it is a background frame. Among them, snr n Indicates the signal-to-noise ratio of the nth frame, and alpha2 is a set threshold. For clarity of description, the threshold alpha2 is referred to as the fourth threshold in the embodiment of the present invention, and the value of alpha2 can be any value, such as in some implementations Take alpha2=15.
[0097] snr in the embodiment of the present invention n It can be obtained in a variety of ways, and in some implementations it can be obtained in the following ways:
[0098] 1. Calculate the spectrum energy Ef of the current signal frame,
[0099] Ef = 1 Mf Σ k = 0 ...
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Abstract
The embodiment of the invention discloses signal classifying method and device, wherein the signal classifying method comprises the steps of: obtaining a frequency spectrum fluctuating parameter of a current signal frame determined as a foreground frame and caching; obtaining a spectrum flocculating variance of the current signal frame according to frequency spectrum fluctuating parameters of all cached signal frames and caching; and obtaining the proportion that the spectrum fluctuating variance is more than the first threshold in all cached signal frames, if the proportion is more than the second threshold, using the current signal frame as a voice frame and if the proportion is less than the second threshold, using the current signal frame as a music frame. The embodiment of the invention is used for judging the signal classification by adopting the signal spectrum fluctuating variance as a signal classifying parameter and using a local statistic method, thereby realizing the signal classification with less parameters, simpler logical relation and lower complexity.
Description
technical field [0001] The present invention relates to the field of communication technology, in particular to a signal classification method and device. Background technique [0002] Speech coding technology can compress the transmission bandwidth of voice signals and increase the capacity of communication systems. With the increasing popularity of the Internet and the further expansion of the communication field, speech coding technology has become one of the most active fields in domestic and international standardization work. With the passage of time, speech encoders are developing towards multi-bit rates and broadband, and their input signals are also diversified, not limited to voice, but also include other signals such as music, and people are concerned about the quality of calls, especially music. Signal quality requirements are constantly improving. For different input signals, encoders that can use different code rates, or even different core encoding algorithms...
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