Coarse emotion soft cutting and classification method for waveform music
A classification method and soft cutting technology, applied in speech analysis, speech recognition, instruments, etc., can solve inconvenient problems
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0054] figure 1 is a simplified process of the rough affective domain of the present invention; figure 2 It is the primary and secondary soft cutting of the music segment of the present invention; image 3 It is the change situation of the segment where the adjacent note comparison item of the present invention is located; Figure 4 It is a schematic diagram of the jump conditions of different coarse emotional domains in the present invention; Figure 5 It is a flowchart of identification steps of the present invention; Image 6 It is a soft cutting flow chart of the soft cutting process in the identification step of the present invention; Figure 7 It is a secondary soft cutting flow chart of the soft cutting process in the recognition step of the present invention; as shown in the figure: a kind of waveform music rough emotion soft cutting classification method provided by the present invention comprises the following steps:
[0055] S1: Provide music data, and establish ...
Embodiment 2
[0084] This embodiment 2 describes in detail the specific process of performing coarse emotional soft cutting of waveform music:
[0085] The music feature extraction step includes a Mallat algorithm-based time-frequency domain rapid decomposition step and a music feature extraction step.
[0086] Time-frequency domain fast decomposition steps based on Mallat algorithm:
[0087] Wavelet transform is a time domain-frequency domain analysis method. This method overcomes the shortcomings of the FFT method using uniform resolution for high and low frequencies. , low-frequency use of different resolution requirements. When the parameter becomes larger, the center frequency becomes smaller, the time-domain bandwidth becomes wider, the frequency-domain bandwidth becomes narrower, the time-domain resolution becomes smaller, and the frequency-domain resolution becomes larger. When the parameter becomes smaller, the center frequency becomes larger, and the time-domain The bandwidth be...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com