Improved DCNN music genre classification method

A classification method and music technology, applied in speech analysis, neural learning method, electro-acoustic musical instruments, etc., can solve the problems of increasing model complexity and increasing the computational burden of model structure, and achieve obvious performance gain and simple calculation.

A classification method and music technology, applied in speech analysis, neural learning method, electro-acoustic musical instruments, etc., can solve the problems of increasing model complexity and increasing the computational burden of model structure, and achieve obvious performance gain and simple calculation.

CN112466329APending Publication Date: 2021-03-09LIAONING TECHNICAL UNIVERSITY
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  • Improved DCNN music genre classification method
  • Improved DCNN music genre classification method
  • Improved DCNN music genre classification method

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Embodiment Construction

[0034]The specific implementation of the present invention will be described in detail below in conjunction with the accompanying drawings. As a part of this specification, the principles of the present invention will be described through examples. Other aspects, features and advantages of the present invention will become clear through the detailed description. In the referenced drawings, the same reference numerals are used for the same or similar components in different drawings.

[0035] Such as figure 1 Shown, the music genre classification method of improved DCNN of the present invention is characterized in that, comprises the following steps:

[0036] Step 1: Input training set and verification set;

[0037] Step 2: Extract audio information MFCC features;

[0038] Step 3: generate spectrum;

[0039] Step 4: Spectrum cutting;

[0040] Step 5: Input the network model;

[0041] Step 6: Train the model;

[0042] Step 7: Verify the model;

[0043] Step 8: Whether the...

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Abstract

The invention discloses an improved DCNN music genre classification method. The method comprises the steps of inputting a training set and a verification set, extracting MFCC features of the audio information, generating a frequency spectrum, carrying out frequency spectrum cutting, inputting a network model, training a model, verifying the model, judging whether a specified batch is reached or not, and outputting a model. According to the method, self-adaption of channel dimensions is achieved through a function, so that the coverage range of interaction of local area cross channels is ensured, an ECA module is more effectively integrated into an existing DCNN architecture, obvious performance gain is brought to a network model, and then the working efficiency of music genre classification is improved. Through the Mel-frequency cepstrum coefficient, the perception characteristics of a human auditory system are simulated, and the classification precision is further improved.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral image classification, and in particular relates to an improved DCNN music genre classification method. Background technique [0002] At this stage, traditional music genre classification methods have been slowly replaced by deep learning methods. Compared with traditional methods, the method of genre classification through feature learning and deep structure mainly has the following advantages: (1) avoiding too professional music theory knowledge and technology, and does not require users to design artificial features; (2) has good end-to-end learning It has certain advantages in solving problems related to music genres; (3) It can greatly reduce the burden on professionals and improve related work efficiency. [0003] Deep Convolutional Neural Network (DCNN), as one of the most widely used deep learning models in the field of music genre classification, has also achieved good application r...

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Application Information

Patent Timeline
09 Mar 2021
Publication
CN112466329A
IPC
G10L25/30; G10L25/51; G10L25/24; G10L25/18; G06N3/08; G06N3/04; G06K9/62
CPC
G10L25/30; G10L25/18; G10L25/24; G10L25/51; G06N3/08; G10H2210/036; G06N3/048; G06N3/045
Inventors
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