An Automatic Classification Method for Multiple Music Genres by Integrating Emotions and Intelligent Algorithms

Figures & data

Figure 1. Block diagram of methodology.

Figure 2. Comparison of the reconstruction performance of each matrix.

Figure 3. Time domain diagram of frequency hopping music spectrum.

Figure 4. Measure values of frequency hopping music spectrum.

Figure 5. L-p geometric representation.

Figure 6. Time-frequency diagram of frequency hopping music spectrum.

Figure 7. Time-frequency diagram of reconstructed frequency hopping music spectrum.

Figure 8. Time-frequency diagram of music spectrum.

Table 1. Validation of the effect of automatic classification method of music genres integrating emotion and intelligent algorithms.

Number	Classification effect	Number	Classification effect
1	88.82	18	88.81
2	88.26	19	89.16
3	89.80	20	91.51
4	87.07	21	87.51
5	88.33	22	89.33
6	91.92	23	90.30
7	86.46	24	88.87
8	89.72	25	88.33
9	84.31	26	84.61
10	84.89	27	88.03
11	86.08	28	90.25
12	90.12	29	89.13
13	85.64	30	88.63
14	91.56	31	85.72
15	85.19	32	89.17
16	89.77	33	87.52
17	84.46	34	90.19

Data Availability Statement

The dataset used to support this study are available from the corresponding author upon request.