Research on Music Classification Technology Based on Deep Learning

Abstract

With the advent of the digital music era, digital audio sources have exploded. Music classification (MC) is the basis of managing massive music resources. In this paper, we propose a MC method based on deep learning to improve feature extraction and classifier design based on MIDI (musical instrument digital interface) MC task. Considering that the existing classification technology is limited by the shallow structure, it is difficult for the classifier to learn the time sequence and semantic information of music; this paper proposes a MIDIMC method based on deep learning. In the experiment, we use the MC method proposed in this paper to achieve 90.1% classification accuracy, which is better than the existing classification method based on BP neural network, and verify the music with its classification accuracy. The key point is that the music division method used in this paper has correct MC efficiency. However, due to the limited ability and time involved in the interdisciplinary field, the methodology of this paper has certain limitations, which still needs further research and improvement.

TIM‐Net: A multi‐label classification network for TCM tongue images fusing global‐local features

“…Through counting the number of pixels of different pixel values in the tongue moss area, the classification of the moss color was achieved with the maximum number of pixels. Zhang [17] adopted an improved K‐means clustering algorithm to separate the moss of tongue images, and then a convolutional neural network based on depth‐separable convolution and skip connections was utilized to classify the moss colors.…”

Musical Instrument Identification Using Deep Learning Approach

“…The first consists of extracting a feature vector (FV) containing audio descriptors and using the baseline machine learning algorithms [12,[15][16][17][18][19][20][21][22][23][24][25][26][27]. The second is based on the 2D audio representation and a deep learning model [28][29][30][31][32][33][34][35][36][37][38][39][40][41], or a more automated version when a variational or deep softmax autoencoder is used for the audio representation retrieval [32,42]. Therefore, by employing machine learning, it is possible to implement a classifier for particular genres or instrument recognition.…”

“…Reviewing the literature that describes the classification of musical instruments, it can be seen that this has been in development for almost three decades [17,18,25,28,36,41]. These works use various sets of signals and statistical parameters for the analyzed samples, standard MPEG-7 descriptors, spectrograms, mel-frequency cepstral coefficients (MFCC), or constant-Q transform (CQT)-the basis for their operation.…”

“…For example, the cello and saxophone are often encountered in jazz music, whereas the banjo is almost exclusively associated with country music. In music genre classification, several well-known techniques have been used, such as SVM (support vector machine) [14,19,25,26,33], ANN (artificial neural networks) [24,40], etc., as well as CNN (convolutional neural networks) [28,30,[34][35][36][37][38][39][40], RNN (recurrent neural networks) [28,41], and CRNN (convolutional recurrent neural network) [31,34].…”

“…Table 1 shows an overview of various algorithms and tasks described above along with the obtained results [14][15][16][18][19][20][21][22][23][24][25][26][27][28][29][30][31][33][34][35][36][37][38][39][40][41]. A system that can listen to the musical instrument tone and recognize it (no metrics shown) Lee J., Kim T., Park J., Nam J.…”