An Acoustic Scene Clustering Method by Jointly Optimizing Deep Transformation Features and Clustering Process
A technology of joint optimization and clustering method, applied in speech analysis, instrumentation, etc., can solve problems such as inability to obtain clustering results of acoustic scenes
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[0065] like figure 1 as shown, figure 1 It is a flowchart of an embodiment of an acoustic scene clustering method for joint optimization of deep transformation features and clustering process, which mainly includes the following processes:
[0066] S1. Extract logarithmic mel spectrum features: pre-emphasize, frame, and window the audio samples of various sound scenes, and then extract the logarithmic mel spectrum of each audio frame;
[0067] S2. Initialize various types and convolutional neural networks: use each sample as an initial class, initialize and generate a convolutional neural network for extracting deep transformation features;
[0068] S3. Update the convolutional neural network and extract new deep transformation features: update the convolutional neural network parameters according to class labels and various samples, and use the updated convolutional neural network to extract deep transformation features of various samples;
[0069] S4. Merge the two most si...
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