Noise suppression method and mobile terminal
A noise suppression, mobile terminal technology, applied in speech analysis, speech recognition, instruments, etc., can solve the problem of high noise delay and achieve the effect of reducing delay
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no. 1 example
[0030] refer to figure 1 , which shows a flowchart of a noise suppression method according to an embodiment of the present invention, which is applied to a mobile terminal, and the method may specifically include the following steps:
[0031] Step 101, identifying the audio signal output by the application program to determine the audio type;
[0032] Wherein, the application program may be various software capable of outputting audio, such as music player software, video player software, and the like. In order to suppress noise in the audio output by the application program, it is first necessary to determine the type of audio output by the application program, such as voice, music, and the like.
[0033] Step 102, determining a noise tracking method corresponding to the audio type;
[0034] Step 103, using the noise tracking method to perform noise tracking on the audio signal to obtain noise energy of the audio signal;
[0035] Wherein, a noise tracking method correspond...
no. 2 example
[0040] refer to figure 2 , which shows a flowchart of a noise suppression method according to an embodiment of the present invention, which is applied to a mobile terminal, and the method may specifically include the following steps:
[0041] Before explaining the flow of the noise suppression method in this embodiment, here, for the convenience of readers to understand the technical solution of the embodiment of the present invention, firstly, a brief description of the preset neural network model in step 202 of the embodiment of the present invention is given.
[0042] In one embodiment, the preset neural network model can be a backpropagation (BP, back propagation) neural network model, or a convolutional neural network model (CNN), and of course is not limited to the two neural networks listed here model, and can also be other types of neural network models. Wherein, it should be noted that the preset neural network model here is a neural network model capable of recogni...
no. 3 example
[0088] refer to Figure 5 , shows a block diagram of a mobile terminal according to an embodiment of the present invention. The mobile terminal in the embodiment of the present invention can realize the details of the noise suppression method in the first embodiment to the second embodiment, and achieve the same effect. Figure 5 The mobile terminals shown include:
[0089] The identification module 51 is used to identify the audio signal output by the application program and determine the audio type;
[0090] A determining module 52, configured to determine a noise tracking method corresponding to the audio type;
[0091] A tracking module 53, configured to perform noise tracking on the audio signal by using the noise tracking method, and obtain noise energy of the audio signal;
[0092] A suppression module 54, configured to suppress the noise energy of the audio signal.
[0093] Optionally, refer to Image 6 ,exist Figure 5 On the basis of , the identification module...
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