Guqin music evaluation standard system and playing style library construction method

Abstract

The invention discloses a guqin music evaluation standard system based on multi-modal perception and a playing style library construction method, and relates to the technical field of music information retrieval and cultural heritage digitization. According to the system, vibration, acoustics and visual data of Guqin playing are synchronously collected through a multi-modal hardware group (including a laser Doppler vibration meter, a 48-channel acoustic camera and an infrared motion capture system), unified feature vectors are generated through fusion in combination with a graph convolutional network (GCN), and a quantitative evaluation system covering harmonic distortion, overtone attenuation slope, pie voiceprint indexes and the like is constructed. Acoustic feature fingerprints of the pie (Guangling pie, Huangshan pie and the like) are extracted based on deep learning, an interpretable genre style model is established, and cross-space-time playing similarity analysis and style migration are supported. A reduced character score dynamic analysis engine is innovatively integrated, an ancient book music score is converted into an intelligent music score with strength and tone marks through OCR and knowledge graph technologies, and immersive teaching of genre characteristics is realized in combination with VR / AR.

Description

Technical Field

[0001] The present invention relates to the technical field of music information retrieval and cultural heritage digitization, and specifically to a professional-level guqin music evaluation system that integrates multimodal acoustic perception, artificial intelligence analysis, and standard quantitative evaluation. By constructing a scientific acoustic feature indicator system and a guqin style model, the objective evaluation of guqin performance quality and the digital inheritance of traditional schools can be achieved. Background Art

[0002] The current evaluation of guqin music mainly relies on subjective auditory experience, and has the following problems: single evaluation dimension: existing technologies (such as CN113314087A) only rely on audio spectrum analysis, ignoring multiple information such as vibration modes and playing techniques; vague school characteristics: the acoustic characteristics of traditional guqin schools (Guangling School, Yushan School, etc.) lack quantitative standards, resulting in disordered style inheritance; poor data comparability: different recording environments and equipment make cross-sample analysis invalid. Summary of the invention

[0003] The guqin music evaluation standard system architecture of the present invention is divided into a hardware layer, a hardware layer, an indicator layer, and an algorithm layer.

[0004] The hardware layer includes core equipment and extended equipment. Core equipment: multi-modal sound pickup adaptive guqin sound amplification base (invention patent basis), integrated vibration sensor, acoustic microphone and environmental monitoring module; extended equipment: laser Doppler vibrometer (LDV): non-contact measurement of panel vibration mode (accuracy 0.1μm); high-precision acoustic camera (48-channel array): spatial sound field reconstruction, positioning resonance energy distribution; infrared motion capture system: tracking the right hand support angle and left hand yinhua amplitude (accuracy ±0.5°).

[0005] The data layer is a multimodal database that includes acoustic data, visual data, and environmental data. Acoustic data: full-band recording (24bit / 192kHz), vibration spectrum (20Hz-20kHz); visual data: performer's fingering trajectory, piano body vibration heat map; environmental data: temperature and humidity, background noise (in accordance with ISO 3382-3 standard).

[0006] The index layer includes basic acoustic indicators and genre characteristic indicators. Basic acoustic indicators: timbre characteristics: harmonic distortion (THD < 1%), overtone attenuation slope (Δt = 30ms @ 3kHz); dynamic range: playing intensity gradient (ppp-fff corresponds to 30-100dB SPL); pitch stability: micro-fluctuation of notes (±5 cents is excellent). Genre characteristic indicators: For example, Guangling School: the thickness of scattered sound (high energy proportion of 200-400Hz); Yushan School: chanting frequency (4-6Hz vibration rate); Mei'an School: finger rolling density (number of string touches per second ≥ 8 times).

[0007] The algorithm layer includes a multimodal feature fusion model and a Qin style classifier. Multimodal feature fusion model: uses a graph convolutional network (GCN) to associate vibration, acoustic, and visual data; generates a unified feature vector (dimension 512) to characterize the overall quality of the performance. Qin style classifier: Based on deep metric learning, it constructs a genre feature space; supports similarity retrieval (such as the similarity score between the performance of "Liushui" and Guan Pinghu's version).

[0008] The method of constructing a Qin style library includes three aspects: data collection specifications, feature extraction process, and style modeling and verification.

[0009] Data collection specifications: Sample selection: covering representative repertoires of the nine major qin schools (≥50 hours of high-quality recordings for each school); Recording conditions: ambient noise ≤NR-20, temperature and humidity 22±2℃ / 50±5%RH; use standard excitation force (0.5N hammer) to record open string sounds as a benchmark.

[0010] Feature extraction process. Acoustic features: Short-time Fourier transform (STFT) extracts the time-frequency spectrum; Mel-frequency cepstral coefficients (MFCC) characterize the timbre texture. Vibration features: Resonant peak extraction (the first three modal frequencies of the panel f1=80Hz, f2=220Hz, f3=450Hz); decay time (T60) measures the resonance persistence of the piano body. Performance technique features: left-hand note trajectory curvature analysis; right-hand string contact angle-sound intensity mapping model.

[0011] Style modeling and verification. Unsupervised clustering: t-SNE dimensionality reduction visualization, verifying the spatial separability of genre features; cluster purity ≥ 90% (Guangling School / Shu School / Zhucheng School). Cross-genre comparability: design style transfer model (CycleGAN), verify feature robustness; expert blind test accuracy ≥ 85%.

[0012] The innovation of this invention lies in the establishment of the first guqin evaluation system that integrates LDV vibration measurement and acoustic array, breaking through the limitations of single modal analysis; pioneering the concept of guqin school acoustic fingerprint, realizing the digital "gene" archiving of cultural heritage; proposing 12 quantitative acoustic indicators, filling the gap in international standards for guqin professional evaluation; constructing an explainable model of guqin school style to help make traditional music education more scientific; providing an objective basis for guqin grading, instrument production, and restoration; and driving innovative business models of "AI+traditional culture" (such as virtual guqin school inheritors). BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 : Guqin music evaluation standard system architecture diagram (hardware layer - data layer - algorithm layer) Figure 2 : Schematic diagram of the dynamic annotation interface of the reduced notation DETAILED DESCRIPTION

[0014] Teaching scenario: A student plays the third paragraph of "Pingsha Luoyan", and the "fu" technique is not powerful enough: the reduced character "fu" flashes red; the force histogram on the right shows the current value of 2.1N (target ≥3.0N); the bottom prompt: "It is recommended to increase wrist force, refer to Li Xiangting's demonstration video."

[0015] Research scenario: Scholars compare the Guangling / Yushan versions of Liushui: Select "Qin School Comparison Mode", the interface splits the screen to display the two schools' reduced-character notations; the "Gunfu" section of the Guangling version is marked in dark red (energy intensity +15%); generate a difference report: "The frequency of the Yushan School's Yinhua is 2.3Hz higher."

Claims

1. A guqin music evaluation standard system and a performance style library construction method, characterized in that: It includes a multimodal perception hardware group, an acoustic feature indicator system and a piano style classification model, wherein: the multimodal perception hardware group includes a laser Doppler vibrometer (LDV), a 48-channel acoustic camera and an infrared motion capture system; the acoustic feature indicator system covers harmonic distortion, overtone attenuation slope, dynamic range gradient and piano school-specific soundprint indicators; the piano school style classification model constructs a genre feature space based on deep metric learning, and supports style similarity calculation and migration.

2. The system according to claim 1, characterized in that The laser Doppler vibrometer (LDV) has a non-contact measurement accuracy of 0.1μm and a frequency range of 10Hz-20kHz, generating a three-dimensional energy cloud map of the panel vibration and aligning it with the acoustic data in time and space.

3. The system according to claim 1, characterized in that The 48-channel acoustic camera adopts a spherical array layout with an aperture of 1.2m, supports spatial sound field reconstruction and resonance energy distribution thermal map generation, and has a positioning accuracy of ±2cm.

4. The system according to claim 1, characterized in that The infrared motion capture system tracks the right hand support angle and the left hand movement amplitude, with a data sampling rate of ≥240Hz, an angle resolution of ±0.5°, and is hard synchronized with the vibration signal through FPGA.

5. The system according to claim 1, characterized in that The reduced-character score parsing engine is implemented by the following steps: high-precision OCR scanning and semantic segmentation of Shen Qi Mi Pu and Wu Zhi Zhai Qin Pu; Based on the knowledge graph, the symbols of the reduced notation are associated with the performance rules (such as the force threshold of the "locking bell" technique); multimodal performance data is combined to generate dynamic music scores, marking the parameters of dynamics, timbre and genre style.

6. The system according to claim 5, characterized in that The dynamic music score supports the "genre filter" function, which converts the same piece of music into different piano styles by adjusting the acoustic feature weights (such as performing "Flowing Water" in the Shu style).

7. The system according to claim 1, characterized in that The digital museum system includes: a voiceprint database: storing the acoustic feature vectors and three-dimensional vibration modes of representative repertoires of each school of guqin; a VR interaction module: immersively experiencing the differences in the performance of "Xiaoxiang Shuiyun" by different schools through a head-mounted display device; a holographic projection system: restoring the performance images of deceased guqin players, and AI comparing student data to generate improvement suggestions.

8. The system according to claim 1, characterized in that The application of the system in guqin production and restoration includes: evaluating the aging degree of Paulownia wood through panel vibration modal analysis (f1=80Hz, f2=220Hz, f3=450Hz); optimizing the Yueshan / Longyin shape based on acoustic indicators to improve the consistency of tone.

9. The system according to claim 1, characterized in that The evaluation standard system is compatible with Chinese plucked string instruments such as the guzheng and se, and can be applied across instruments by replacing sensor layout and acoustic feature templates.

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