Article ; Online: Music Similarity Detection Guided by Deep Learning Model.
Computational intelligence and neuroscience
2023 Volume 2023, Page(s) 1263620
Abstract: Digital music has become a hot spot with the rapid development of network technology and digital audio technology. The general public is increasingly interested in music similarity detection (MSD). Similarity detection is mainly for music style ... ...
| Abstract | Digital music has become a hot spot with the rapid development of network technology and digital audio technology. The general public is increasingly interested in music similarity detection (MSD). Similarity detection is mainly for music style classification. The core MSD process is to first extract music features, then implement training modeling, and finally input music features into the model for detection. Deep learning (DL) is a relatively new feature extraction technology to improve the extraction efficiency of music features. This paper first introduces the convolutional neural network (CNN) of DL algorithms and MSD. Then, an MSD algorithm is constructed based on CNN. Besides, the Harmony and Percussive Source Separation (HPSS) algorithm separates the original music signal spectrogram and decomposes it into two components: time characteristic harmonics and frequency characteristic shocks. These two elements are input to the CNN together with the data in the original spectrogram for processing. In addition, the training-related hyperparameters are adjusted, and the dataset is expanded to explore the influence of different parameters in the network structure on the music detection rate. Experiments on the GTZAN Genre Collection music dataset show that this method can effectively improve MSD using a single feature. The final detection result is 75.6%, indicating the superiority of this method compared with other classical detection methods. |
|---|---|
| MeSH term(s) | Deep Learning ; Music ; Algorithms ; Digital Technology ; Neural Networks, Computer |
| Language | English |
| Publishing date | 2023-02-20 |
| Publishing country | United States |
| Document type | Journal Article ; Retracted Publication |
| ZDB-ID | 2388208-6 |
| ISSN | 1687-5273 ; 1687-5273 |
| ISSN (online) | 1687-5273 |
| ISSN | 1687-5273 |
| DOI | 10.1155/2023/1263620 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.