CN108122562A - A kind of audio frequency classification method based on convolutional neural networks and random forest - Google Patents
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Abstract
The invention discloses a kind of audio frequency classification method based on convolutional neural networks and random forest, this method includes：S1:Spectrum analysis is carried out to original audio data collection, including segmentation, framing, adding window, Fourier transformation, obtains the corresponding spectrogram of original audio file；S2:Spectrogram to obtain trains a convolutional neural networks feature extractor as input；S3:Remove the softmax layers of convolutional neural networks, extract the high-level characteristic of spectrogram；S4:Random forest grader is trained using the spectrogram high-level characteristic of extraction；S5:Based on the high-level characteristic of convolutional neural networks extraction, audio classification is carried out using trained random forest.The present invention is based on convolutional neural networks to do feature extraction, avoid the complicated processes of construction extraction feature manually, simultaneously for causing generalization ability insufficient as convolutional neural networks grader using softmax the problem of, the softmax layers of convolutional neural networks are replaced using random forest, as final grader.Higher accuracy rate and recall rate are achieved during the test.
Description
Technical field
The invention belongs to machine learning fields, are related to a kind of audio classification side based on convolutional neural networks and random forest
Method.
Background technology
The life of internet and the development let us of multimedia technology is flooded with substantial amounts of audio, especially various music nets
It stands, possesses the audio file of substantial amounts and different style.In face of the audio of magnanimity, audio retrieval can help us quick and precisely
Find required audio file in ground.Audio classification is the premise of audio retrieval, but carries out manual sort to a large amount of audio files
It is a quite time-consuming and a hard row to hoe.With the auditory fatigue of people, the accuracy rate of manual sort also can decrease.For
A large amount of audio files, fast and accurately automatic classification seem very it is necessary to.Research in relation to audio frequency classification method is more, such as
Using the two-stage audio frequency classification method based on hidden Markov model and support vector machines mixing, first with hidden Markov model
Preliminary classification is carried out to audio, most probable two kinds of classification results is determined, then is made finally of corresponding support vector machine classifier
Judgement.The method that similarity between also with good grounds audio content classifies to audio, being represented with the pitch collection of each audio should
Audio file, with LDA topic models to audio classification.Also have and carried out using gauss hybrid models, decision tree etc. as grader
Classification.But these methods craft construction feature mostly by the way of traditional, both cumbersome, the feature of extraction is also not enough.
And single grader is used, the generalization ability for causing model is not strong.
In recent years, deep learning is gradually burning hot, and structure contains more hidden layers, more abstract by combining low-level image feature formation
It is high-rise represent attribute or feature, preferably feature can be represented by the distributed of mining data, than the side of traditional manual construction feature
Formula effect is more preferable.For present situation and the above problem, it is necessary to design a kind of audio frequency classification method based on deep learning.
The content of the invention
The technical problems to be solved by the invention are to provide a kind of audio based on convolutional neural networks and random forest point
Class method, this method automatically extract high-level characteristic using convolutional neural networks, and it is extensive to solve single grader using random forest
The problem of indifferent, has higher accuracy rate and recall rate.
Inventive technique solution is as follows：
A kind of audio frequency classification method based on convolutional neural networks and random forest, comprises the following steps.
Step 1：Spectrum analysis is carried out to original audio file, obtains its corresponding spectrogram.Due to audio file often
Longer, it is excessive directly to do the spectrogram that spectrum analysis obtains to original audio, cause later stage training pattern occupying system resources compared with
It is more.So appropriate segmentation is taken original audio, then spectrum analysis is done to every section audio, including framing, in short-term adding window, Fourier
The processes such as conversion.Assuming thatIt is a long sequence,It is the window function that length is N, usesTo addingAdding window obtains N
Point sequence, i.e.,
Have on frequency domain
The formula of Short Time Fourier Transform is as follows：
WhereinFor original signal,For window function.By spectrum analysis, the corresponding spectrogram of audio has been obtained.
Step 2：The spectrogram obtained by the use of in step 1 trains an improved convolutional neural networks as training set.It should
Network has 14 layers, including convolutional layer, down-sampling layer, Dropout layers, Flatten layers, full articulamentum, Batch
Normalization layers, softmax layers etc., using cross entropy as loss function.Each layer is described as follows：
Input：Size is the spectrogram of 248*248；
Layer1:Convolutional layer, core size are (5,5), and 64, strides=1, output characteristic figure size is (244,244)；
Layer2:Down-sampling layer, core size are (2,2), and output characteristic figure size is (122,122)；
Layer3:Convolutional layer, core size are (3,3), and 128, strides=2, output characteristic figure size is (60,60)；
Layer4:Down-sampling layer, core size are (2,2), and output characteristic figure size is (30,30)；
Layer5:Convolutional layer, core size are (3,3), and 256, strides=2, output characteristic figure size is (14,14)；
Layer6:Down-sampling layer, core size are (2,2), and output characteristic figure size is (7,7)；
Layer7:Convolutional layer, core size are (2,2), and 512, strides=1, output characteristic figure size is (6,6)；
Layer8:Down-sampling layer, core size are (2,2), and output characteristic figure size is (3,3)；
Layer9:Dropout layers, dropout=0.5 makes neuron fail by certain probability, prevented plan in the training process
It closes；
Layer10:Flatten layers, multidimensional data one-dimensional, it is transitioned into full articulamentum；
Layer11:Full articulamentum, output neuron number are 128；
Layer12:Batch Normalization, normalize input signal, while keep the ability to express of model again；
Layer13:Full articulamentum, output neuron number is 9, because the data set sample used has 9 classes；
Layer14:Softmax layers, grader is exported as final probability distribution, and each value represents a kind of probability of classification.
Step 3：The softmax layers of trained convolutional neural networks in step 2 are removed, by the last one full articulamentum
High-level characteristic of the output as spectrogram.
Step 4：Random forest grader is trained using the high-level characteristic extracted in step 3.Using Gini impurity level conducts
The criterion of decision tree feature selecting.Algorithm description is as follows：
Input：Sample set D={ (x1, y1), (x2, y2) ... (xm, ym) }, Weak Classifier iterations T；
Output：Final strong classifier f (x)；
The T for t=1,2 ...
A) concentrated from initial data and carry out the t times stochastical sampling, sampled m times altogether, obtain sampling set Dm；
B) m-th of decision tree Gm (x) is built using sampling set Dm.A part of feature is randomly choosed in all features of sample, so
An optimal feature is selected from these features again afterwards and divides left and right subtree for decision tree.
Step 5：Audio to be sorted is subjected to the spectrum analysis in step 1 and obtains spectrogram, is then removed in step 3
The high-level characteristic of extraction, is finally input in step 4 and instructs by softmax layers of convolutional neural networks extraction spectrogram high-level characteristic
The random forest grader perfected carries out audio classification, and the classifications of the most polls launched by the use of T weak learners is as final class
Not.
The present invention is based on deep learnings to propose a kind of audio frequency classification method, employs convolutional neural networks and random forest
The mixed model being combined.For conventional model it is insufficient to feature extraction the problem of, the present invention converts the audio into spectrogram,
The high-level characteristic of convolutional neural networks extraction spectrogram is recycled, has given full play to powerful feature of the convolutional neural networks to image
Extractability simplifies the complex process of feature extraction.For single grader generalization ability it is not strong the problem of, employ random
Forest model, the advantages of giving full play to random forest integrated study, structure more decision trees are classified, and compensate for single grader
Deficiency.From classification results, the present invention has higher accuracy rate and recall rate.
Description of the drawings
Fig. 1 is a kind of flow chart of the audio frequency classification method based on convolutional neural networks and random forest of the present invention.
The spectrogram obtained after Fig. 2 spectrum analyses.
Fig. 3 is the flow chart that high-level characteristic extraction is carried out using improved convolutional neural networks.
Specific embodiment
With reference to the accompanying drawings and examples, the specific implementation method of the present invention is described further.Example is applied below only to use
In illustrating the present invention, but it is not limited to the scope of the present invention.
Embodiment 1 is a kind of example of the present invention, using " GTZAN Genre Collection " as data set, using it
In nine kinds of different schools audio file as training set and test set, nine kinds of classifications are：blues、C1assical、
Country, Disco, Jazz, Metal, Pop, Reggae and Rock.
1. audio file is divided into isometric 6 sections, each section all corresponds to identical label.To each section audio framing, add
Window, Fourier transformation obtain its spectrogram.What attached drawing 2 was shown is the spectrogram obtained.Spectrogram is read in, is converted to ash
Degree figure.It is again 248*248 by the size adjusting of every figure.The pixel value of the picture after adjustment is finally saved in array, as
A sample in convolutional neural networks data set.By operation above, data set D (5400,248,248) is obtained, is represented
There are 5400 spectrograms, the width of every spectrogram is 248, is highly 248.Data set is divided into training set and test set,
Wherein 80% is used as training set, and 20% is used as test set, finally obtains training set T (4320,248,248), test set V (1080,
248,248)。
2. utilize training set T (4320,248,248) training convolutional neural networks model.Network has 14 layers altogether, including volume
Lamination, down-sampling layer, full articulamentum, Dropout layers, Normalization layers of Batch etc..
3. after the completion of convolutional neural networks training, remove last softmax layers.With trained convolutional Neural net
Network carries out deeper feature extraction to spectrogram, by the original training set T (4320,248,248) being made of spectrogram weight
Structure is new training set T ' (4320,9), the original test set V (1080,248,248) being made of spectrogram is reconstructed into new
Test set V ' (1080,9).
4. random forest is trained with new training set T ' and test set V ', as final grader.Using different ginsengs
Number combination settings, wherein
| Parameter | Numerical value |
| n_estimators | [10,50,100] |
| min_samples_split | [2, 3, 4] |
| min_samples_leaf | [1, 2, 3] |
By selecting, optimal parameter is combined as n_estimators:100, min_samples_split:3, min_samples_
leaf:1.After the completion of random forest training, tested on test set, it is as a result as follows：
| Classes | Precision | Recall | F1-score | support |
| 0 | 0.80 | 0.74 | 0.77 | 118 |
| 1 | 0.89 | 0.92 | 0.90 | 133 |
| 2 | 0.75 | 0.80 | 0.78 | 117 |
| 3 | 0.75 | 0.83 | 0.79 | 118 |
| 4 | 0.93 | 0.88 | 0.90 | 134 |
| 5 | 0.94 | 0.90 | 0.92 | 108 |
| 6 | 0.88 | 0.85 | 0.87 | 103 |
| 7 | 0.86 | 0.78 | 0.82 | 124 |
| 8 | 0.64 | 0.68 | 0.66 | 125 |
| Avg/total | 0.83 | 0.82 | 0.82 | 1080 |
This method can accurately classify automatically to audio as can be seen from the above table, and wherein Average Accuracy reaches
83%, average recall rate has reached 82%.
Claims (3)
1. a kind of audio frequency classification method based on convolutional neural networks and random forest, feature include the following steps：
Step 1:Spectrum analysis is carried out to original audio data collection, long audio file is divided into isometric several sections first, every section
Audio corresponds to identical label, then carries out framing, adding window, Fourier transformation to every section audio, obtains the frequency spectrum of every section audio
Figure, a sample as new training set；
Step 2:All spectrograms and its corresponding label obtained using step 1, one improved convolutional neural networks of training,
The network has 14 layers；
Step 3:Remove the softmax layers for the convolutional neural networks that step 2 learns, then extract institute with convolutional neural networks again
There is the high-level characteristic of spectrogram；
Step 4:The high-level characteristic of the spectrogram extracted using step 3 trains random forest grader, is made using Gini impurity levels
For the criterion of decision tree feature selecting；
Step 5:Audio to be sorted is subjected to the spectrum analysis in step 1 and obtains spectrogram, is then removed in step 3
The high-level characteristic of extraction, is finally input in step 4 and instructs by softmax layers of convolutional neural networks extraction spectrogram high-level characteristic
The random forest grader perfected carries out audio classification, and final classification results are obtained in a manner of ballot.
2. a kind of audio frequency classification method based on convolutional neural networks and random forest according to claim 1, feature
It is, for audio frequency characteristics, the specific implementation process of this method includes two-stage feature extraction, and first order feature extraction is to pass through frequency
Spectrum analysis obtains the corresponding spectrogram of audio, tentatively extracts its low layer time-frequency characteristics, and second level feature extraction uses improved volume
Product neutral net, further extracts high-level characteristic to spectrogram.
3. a kind of audio frequency classification method based on convolutional neural networks and random forest according to claim 1, feature
Be, this method in order to overcome the problems, such as that softmax causes generalization ability not strong as convolutional neural networks grader, using with
Machine forest replaces last layer of convolutional neural networks, as final audio classifiers.
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| CN108766461A (en) * | 2018-07-17 | 2018-11-06 | 厦门美图之家科技有限公司 | Audio feature extraction methods and device |
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