Abstract
With the development of the Internet of Things, many industries have been on the train of the information age, and digital audio technology is also constantly developing. Music retrieval has gradually become a research hotspot in the music industry. Among them, the auxiliary recognition of music characteristics is also a particularly important Task. Music retrieval is mainly to manually extract music signals, but now the music signal extraction technology has encountered a bottleneck. The article uses Internet and artificial intelligence technology to design an SNN music feature recognition model to identify and classify music features. The research results of the article show (1) statistic graphs of the main melody and accompanying melody of different music. The absolute value of the main melody and accompanying melody mainly fluctuates in the range of 0–7, and the proportion of the main melody can reach 36%. The accompanying melody can reach 17%. After the absolute value of the interval reaches 13, the interval ratio of the main melody and the accompanying melody tends to be stable, maintaining between 0.6 and 0.9, and the melody interval ratio value completely coincides; the main melody in the interval variable is X. (1) The relative difference value in the interval of −X(16) fluctuates greatly. After the absolute value of the interval reaches 17, the interval ratio of the main melody and the accompanying melody tends to be stable, maintaining between 0.01 and 0.04 and the main melody. The value of the difference is always higher than the accompanying melody. (2) When the number of feature maps is 24∗5, the recognition result is the most accurate, MAP recognition result can reach 78.8, and the recognition result of precision@ is 79.2; when the feature map size is 5∗5, the recognition result is the most accurate, MAP recognition result can reach 78.9, the recognition result of precision@ is 79.2, and the recognition result of HAM2 (%) is 78.6. The detection accuracy of the SNN music recognition model proposed in the article is the highest. When the number of bits is 64, the detection accuracy of the SNN detection model is 59.2%, and the detection accuracy of the improved SNN music recognition model is 79.3%, which is better than the detection rate of ITQ music recognition model of 17.9%, which is 61.4% higher. The experimental data further shows that the detection efficiency of the ITQ music recognition model is the highest. (3) The SNN music recognition model proposed in the article has the highest detection accuracy, regardless of whether it is in a noisy or no-noise music environment, with an accuracy rate of 97.97% and a detection accuracy value of 0.88, which is 5 types of music. The highest one among the recognition models, the ITQ music recognition model, has the lowest detection accuracy, with a detection accuracy of 67.47% in the absence of noise and a detection accuracy of 70.23% in the presence of noise. Although there is a certain noise removal technology, it can suppress noise interference to a certain extent, but cannot accurately describe music information, and the detection accuracy rate is also low.
Highlights
(2) When the number of feature maps is 24 ∗ 5, the recognition result is the most accurate, MAP recognition result can reach 78.8, and the recognition result of precision@ is 79.2; when the feature map size is 5 ∗ 5, the recognition result is the most accurate, MAP recognition result can reach 78.9, the recognition result of precision@ is 79.2, and the recognition result of HAM2 (%) is 78.6. e detection accuracy of the SNN music recognition model proposed in the article is the highest
In order to test the performance of the music recognition model, the experiment improved the SNN music recognition model proposed in the article and compared it with the detection performance of the other three models. e experiment chose 5 different types of bit numbers. e number of bits is a unit, and the same as the sampling accuracy, the higher the baud rate or bit rate is, the more detailed the light changes of the music can be reflected
E experimental data further shows that the ITQ music recognition model has the highest detection efficiency, which greatly promotes the efficiency of music feature auxiliary recognition
Summary
Because the network has the advantages of fast information dissemination, easy use, and sufficient network resources, it is widely used in human work and study life. Literature [5] introduced optical music recognition technology and proposed a method for computer to automatically recognize music scores. Literature [9] proposed a music emotion recognition method based on adaptive aggregation regression model. Literature [11] proposed a method to solve the problem of multidimensional music emotion recognition, combining standard and melody audio features. E article studies the impact of the reduction of training real numbers on the detection results in the process of music style recognition. Literature [13] presents a method to parse solo performances into individual note components and use support vector machines to adjust the back-end classifier. E method is to extract effective music information from the music signal and reorganize the music signal to a certain extent, so as to achieve the function of noise reduction. Literature [15] proposed a method for analyzing and recognizing music speech signals based on speech feature extraction. e method is to extract effective music information from the music signal and reorganize the music signal to a certain extent, so as to achieve the function of noise reduction. e results of the experiment show that the reorganized music signal has good noise reduction compared with the original music signal ability
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