Detection system and method for scientific bowing of string-pulling musical instrument

Abstract

The invention belongs to the technical field of acoustic detection, and relates to musical instrument playing (subjective feeling), playing mechanics (biomechanics), musical instrument acoustics and psychoacoustics, in particular to a detection system and method for scientific bowing of string-pulling musical instruments. According to the invention, change rules of myoelectricity, electroencephalogram and sound signals in two playing states, namely instantaneous power generation in a fast muscle relaxed state and continuous excitation of string vibration by inertia, and continuous power generation in a fast muscle fatigue state to excite string vibration, are studied, and corresponding association of the myoelectricity, electroencephalogram and sound signals in different power generation states is analyzed; a player can perform special pronunciation training on the basis of the pronunciation training device and understand the force exerting and pronunciation mechanisms, and a better playing effect is achieved. According to the scientific playing detection method for the string instrument based on interaction of the arm movement information, the myoelectricity, the electroencephalogram and the sound signals, objective evaluation of different force exerting states of a playing main body can be achieved, and a scientific evaluation method is provided for teaching and playing of string instruments.

Description

Technical Field

[0001] The present invention belongs to the field of acoustic detection technology, and relates to musical instrument performance (subjective feeling), performance mechanics (biomechanics), musical instrument acoustics and psychoacoustics, and specifically is a detection system and method for scientific bowing of stringed instruments. Background Art

[0002] When playing stringed instruments, performers often produce tense or uncomfortable sounds. This subjective listening experience has its objective and scientific reasons, that is, the performer's arm force position is not accurate or the force method is improper. Its essence is the performer's wrong understanding of performance mechanics, instrument acoustics and psychoacoustics. From the current teaching practice of stringed instruments, most teachers still stay at the level of subjective experience when teaching, lacking the mechanism of systematic teaching from the objective physiological, psychological and acoustic perspectives. Moreover, the subjective experience and teaching methods of teachers are not equal to the learning ability, understanding ability and talent conditions of students. There are some differences between the two, such as "it can only be understood but not expressed in words", "relaxed shoulders, hanging elbows, cantilevered arms", "exerting force in a relaxed state", "not exerting force" and so on, which are limited to sensibility, or lack of scientific and precise description, which makes it impossible for students (performers) to accurately judge whether their bowing force state is right or wrong during the performance. Therefore, how to break the traditional empirical teaching barriers and build a direct, fast and correct training system between teachers and students is an urgent need to study a scientific technical method as a teaching support to explore the movement principles and correct force generation methods of the arm system of stringed instruments.

[0003] At present, there are two invention patent applications related to the research on bowing of stringed instruments: CN202011355448.7 "An interactive erhu teaching device" and CN201811530887.X "A computer vision-based method for assisting violin practitioners in bowing detection". "An interactive erhu teaching device" discloses an interactive erhu teaching device used in the field of erhu teaching. This device can automatically sense the string pressing operation through the finger position acquisition module, and the bowing acquisition module can receive the bowing operation. By setting the sound playback module, the teaching music and the sound played can be presented. "A method for assisting violin practitioners to detect bowing skills based on computer vision" discloses a method for assisting violin practitioners to detect bowing skills based on computer vision, which detects whether the practitioner's bowing skills are accurate during violin practice, thereby helping teachers to conduct targeted detection and determine whether the practitioner makes mistakes during practice; Chinese invention patent applications related to the research on multi-bioelectric signal electromyography and brain wave interaction include: CN202311654977.0 "A method for motor imagery gesture recognition based on multi-modal fusion of electroencephalogram and electromyography", CN202211347792.0 "A biofeedback system for electroencephalogram, electromyography and pulse wave and its analysis method", CN200810063305.1 "A control method for an electric prosthetic hand jointly controlled by electroencephalogram and electromyography", and CN202211491799.X "A speech stimulation feedback rehabilitation system and method based on synchronization of EEG and EMG". In essence, the above-mentioned related studies firstly failed to find the contradiction between skeletal muscle relaxation and force, that is, relaxation of shoulders and arms during playing means relaxation of skeletal muscles, but how can relaxed skeletal muscles use force to pull the bow? Secondly, they did not realize the intrinsic connection between piano sound, brain nerves and muscle nerves, and did not provide a technical solution to characterize the connection between the three.

[0004] In summary, the study of bowing dynamics of stringed instruments is of great significance in music education, music performance, human body movement mechanics, instrument acoustics and other disciplines. The present invention relates to a detection method for scientific bowing of stringed instruments, which can observe the corresponding correlation between muscles, nerve signals and piano sounds when the performer is in the two playing states of "natural" force and tense force, and form a subjective and objective evaluation through comparative analysis, so that contemporary science and technology can provide scientific and empirical support for artistic practice, and realize the unity of mechanics and acoustics, art and technology, sensibility and rationality, and subjectivity and objectivity. Summary of the invention

[0005] In order to make up for the shortcomings of the existing subjective feeling judgment method, the purpose of the present invention is to provide a detection system and method for scientific bowing of stringed instruments.

[0006] The present invention is achieved by adopting the following technical solutions:

[0007] A detection system for scientific bowing of stringed instruments comprises an electromyography measurement system, an electroencephalography measurement system and an acoustic measurement system.

[0008] The electromyography measurement system uses a wireless surface electromyography system to complete the acquisition and measurement. The equipment includes a data receiver, a wireless surface detector, an electrode sheet, a terminal, and an electromyography acquisition and analysis module. The wireless surface detector and the electrode sheet are placed on the skin surface of the performer to obtain the movement electrical signals of the skeletal muscles, and then input them into the electromyography acquisition and analysis module to analyze the frequency characteristics.

[0009] The EEG measurement system uses a wireless wearable portable EEG system for collection and measurement. The equipment includes conductive paste, electrode cap, signal amplifier, wireless adapter, terminal, collection and recording and EEG signal processing module; the electrode cap is placed on the surface of the performer's scalp to obtain the bioelectric signals released by the nerves, and the frequency characteristics can be analyzed by inputting them into the collection and recording and EEG signal processing module.

[0010] The acoustic measurement system is completed by an acoustic and vibration measurement system for collection and measurement. The equipment includes a microphone, an amplifier, a terminal, and an acoustic collection and analysis module. The microphone is placed on both sides of the performer to obtain sound signals, which are input into the acoustic collection and analysis module to analyze their frequency characteristics.

[0011] A method for detecting the scientific bowing of a stringed instrument comprises the following steps: firstly, a performer draws a bow in an open string state to stimulate the string to vibrate, emit a fixed pitch of the instrument, and keep the sound for more than 20 seconds; secondly, a wireless detector placed on the surface of the performer's skin collects electrical signals of muscle contraction and relaxation, a wireless electrode placed on the surface of the performer's scalp collects electrical signals of brain activity, and at the same time, a microphone placed 1.5 meters on both sides of the performer collects sound signals emitted by the vibration of the string and the instrument body stimulated by the bowing; thirdly, an electromyographic signal is transmitted to a computer acquisition module of a measurement system through a data receiver, an electroencephalogram signal is transmitted to a signal amplifier through a wire in an electrode cap, and then transmitted from the signal amplifier to a computer acquisition module of a measurement system through a wireless adapter; finally, an electromyographic signal is processed by an (EMG-Analyzer) data processing module to generate a spectrum diagram, an electroencephalogram signal is processed by an (EEG Studio) data processing module to form a spectrum diagram, and a sound signal is processed by a (B&K Connect) data processing module, and an FFT signal unit is used to process and calculate the data to form a spectrum diagram.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] (1) The present invention studies the changing patterns of electromyographic, electroencephalographic and sound signals under two playing conditions: the player exerts force instantaneously in a relaxed fast muscle state and continuously stimulates string vibration by inertia (hereinafter referred to as "natural" force), and the player continuously exerts force in a fatigued fast muscle state (hereinafter referred to as tense force) to stimulate string vibration. The corresponding correlation among the three signals under different force states is analyzed. The player can use the present invention as a basis to conduct special pronunciation training, understand the force and sound generation mechanism, and achieve better playing effects.

[0014] (2) The scientific performance detection method of stringed instruments based on the interaction of arm movement information with electromyography, electroencephalography and sound signals can achieve objective evaluation of the different force states of the performer, and provide a scientific evaluation method for the teaching and performance of stringed instruments.

[0015] The invention has reasonable design and good practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flow chart showing the detection method for scientific bowing of stringed instruments.

[0019] Figure 2 The frequency domain diagram of the radial wrist flexor electromyographic signal shows the erhu player's "natural" force vs. tense force.

[0020] Figure 3 The frequency domain diagram of the brachioradialis electromyography signal shows the erhu player's "natural" force vs. tense force.

[0021] Figure 4 The frequency domain diagram of the radial wrist flexor electromyographic signal shows the violin player's "natural" force vs. tense force.

[0022] Figure 5 The frequency domain diagram of the brachioradialis electromyographic signal showing the violin player's "natural" force vs. tense force.

[0023] Figure 6 The spectrum diagram of the sound signal representing the "natural" force exerted by an erhu player.

[0024] Figure 7The spectrum of the sound signal showing the tension of an erhu player.

[0025] Figure 8 The spectrum of the sound signal representing the "natural" force exerted by a violinist.

[0026] Fig. 9 The spectrum of the sound signal representing the violin player's nervousness and exertion.

[0027] Fig.10 The EEG signal spectrum of a violinist's "natural" force.

[0028] Fig.11 The EEG signal spectrum shows the violin player's nervousness and exertion. DETAILED DESCRIPTION

[0029] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the scheme of the present invention will be further described below. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all of the embodiments.

[0031] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0032] A detection system for the scientific bowing of a stringed instrument arm comprises an electromyography measurement system, an electroencephalography measurement system and an acoustic measurement system.

[0033] The EMG measurement system is completed by the BTS FreeEMG1000 wireless surface EMG system, which includes a data receiver, a wireless surface detector, electrodes, a terminal, and an EMG acquisition and analysis module (EMG-Analyzer software). The wireless surface detector and electrodes are placed on the skin surface of the performer to obtain the movement electrical signals of the skeletal muscles, which are then input into the EMG analysis module to analyze the frequency characteristics.

[0034] The EEG measurement system uses Mitsar SmartBCI wireless wearable portable EEG system for collection and measurement. The equipment includes conductive paste, electrode cap, signal amplifier, wireless adapter, terminal, collection and recording and EEG signal processing module (EEGStudio software). The electrode cap is placed on the surface of the performer's scalp to obtain the bioelectric signal released by the nerves, and its frequency characteristics can be analyzed by inputting it into the EEG signal processing module.

[0035] The acoustic measurement system consists of The B&K acoustic and vibration measurement system completes the acquisition and measurement. The equipment includes microphones, amplifiers, terminals, and acoustic acquisition and analysis modules (B&K Connect software). The microphones are placed on both sides of the performer to obtain sound signals, which can be input into the acoustic analysis module to analyze its frequency characteristics.

[0036] The synchronous measurement of electromyography, electroencephalography and sound signals can fully express the entire process from the brain sending bioelectric signals, to the arm skeletal muscles receiving the signals and exerting force, to the bow movement stimulating the vibration of the strings and the body of the piano, and finally the emission of the piano sound.

[0037] A method for detecting the scientific bowing of a stringed instrument arm, such as Figure 1 As shown below:

[0038] The electromyographic signal is collected through the BTS FreeEMG1000 device. Enter the collection software and follow the operating steps to connect the Bluetooth and data receiver. Stick the electrode patch on the surface of the muscle to be tested, place the wireless surface detector on the patch, confirm the connection status of the detector, establish the collection module and set the collection parameters according to the muscle part of the electromyographic signal. The transmission frequency is 2.4GHz ISM band, the input impedance is 100MΩ, the resolution is 16bit, the sampling rate is 1KHz, the sensitivity is 1μV, the accuracy is: ±2%, the RF channel is 25, the collection frequency is 1000Hz, and the EMG channel range is 3.0mV.

[0039] The EEG signal is collected through the Mitsar SmartBCI device. Enter the software to connect the adapter, turn on the amplifier, establish the amplifier template and the acquisition template in the acquisition module, set the acquisition parameters, wear the EEG cap, ensure that each lead is in full contact with the performer's scalp surface, and inject the conductive paste into the corresponding lead port with a syringe. At the same time, preview the data information and detect the electrode impedance, set the acquisition parameters, channel number 24, electrode layout MCSCap21Ch, reference mode REF, storage rate 500Hz, EEG dynamic range -562.5~562.5mV, high-pass filter 0.5Hz, low-pass filter 30Hz, DC acquisition. This embodiment mainly collects EEG signals from 24 electrodes such as C3, C4, and Cz, and sets the average of reference electrodes A1 and A2 as the average value.

[0040] Sound signal through Equipment collection, after calibrating two 4189-A-021 microphones, set them 1.5 meters to the left and right sides of the performer (instrument) to collect the sound signal of the performer (instrument). Before collection, select the corresponding acoustic parameters, the recording time is 250ms, the sampling interval is 15.26us, the time weighting is Hanning window, LinearAll averaging is used, and the overlap rate is 66.7%.

[0041] (2) The performer first uses natural gravity to push and pull the bow for more than 20 seconds, at which time the right arm is in a relaxed state. Then the performer pushes and pulls the bow while exerting force, which lasts for more than 20 seconds, at which time the right arm is in a tense state.

[0042] (3) At the same time, wireless detectors placed on the surface of the performer's skin collect electrical signals of muscle contraction and relaxation, wireless electrodes placed on the surface of the performer's scalp collect electrical signals of brain activity, and microphones placed 1.5 meters on both sides of the performer collect sound signals from bowing, thereby realizing real-time collection of electromyographic signals, electroencephalographic signals and sound signals.

[0043] (4) The collected electromyographic signals are transmitted to the computer acquisition and analysis module through the data receiver, and then processed to extract the time domain and frequency domain characteristics of the electromyographic signals, and analyze and evaluate the power of the human muscle during active contraction. The electromyographic signals are processed by the electromyographic analysis software to form time domain indicators and frequency domain indicators. The time domain indicators include the integrated electromyographic value (IEMG), the average electromyographic value (AEMG), and the root mean square value (RMS). The frequency domain indicators include the mean power frequency (MPF) and the median frequency (MF).

[0044] The collected EEG signals are transmitted to the signal amplifier through the wires in the electrode cap, and then transmitted to the computer acquisition module through the wireless adapter. The analysis software performs data preprocessing on these measured raw data, mainly including importing data, filtering, deleting useless channels, reducing sampling rate, detecting artifacts, segmenting and classifying, correcting baselines, detecting peaks, generating 3D graphics, etc.

[0045] The collected sound signal is imported into the data processing module of B&K Connect software, and the FFT signal unit is used to process and calculate the spectrum. Before calculation, the corresponding acoustic parameters are set, the FFT line number is 6400, the frequency bandwidth is 25.6KHz, and the frequency resolution is 4Hz. It mainly includes importing data, establishing a processing chain, analyzing data, and outputting reports.

[0046] (5) Finally, by comparing the differences in muscle, nerve signals, and sound when bowing under different force states, we can draw a conclusion, which can be used as data support and theoretical basis for scientific bowing judgment of stringed instruments.

[0047] Embodiment 1:

[0048] The embodiment of the present invention selects a general sample example and adopts the above-mentioned system, which is as follows:

[0049] The performers are selected from outstanding graduate students majoring in erhu and violin from a certain university.

[0050] The specific experimental steps are the same as the previous specific implementation method. In the process of collecting electromyographic signals from the performer's right arm, a multi-channel wireless detector is used to set channels 1-8 to collect 8 channels. The 8 groups of muscles selected from the performer are: No. 1-abductor pollicis longus, No. 2-flexor carpi radialis, No. 3-brachioradialis, No. 4-anconeus, No. 5-triceps, No. 6-biceps, No. 7-deltoid, No. 8-brachialis. After repeating the experiment three times, the following experimental results were obtained (see Table 1 and Table 2).

[0051] In the process of collecting EEG signals from the performer’s head, the midpoint of the frontal pole (Fpz), the midpoint of the frontal pole (Fz), the left frontal pole (Fp1), the right frontal pole (Fp2), the central point (Cz), the left center (C3), and the right center (C4) were selected to measure the bioelectric signals. After repeating the experiment three times, the following experimental results were obtained (see Tables 5 and 6).

[0052] Experimental Results

[0053] (1) By observing the time domain related index value comparison table (Table 1, Table 2) and the spectrum diagram ( Figure 2 , Figure 3 , Figure 4 , Figure 5 ), select the muscles related to elbow flexion activity.

[0054] During the first half of the bowing process of the erhu and violin, the right elbow joint flexes and extends. The muscles involved in the flexion and extension of the elbow joint mainly include the biceps, brachialis, brachioradialis, radial flexor carpi and triceps. Elbow flexion and extension are divided into two types: elbow flexion and elbow extension. Taking elbow flexion as an example, the biceps and brachialis are the main muscles for flexing the elbow joint. The brachioradialis and radial flexor carpi can also flex the elbow joint, but their strength is relatively weak.

[0055] Table 1

[0056]

[0057] Table 2

[0058]

[0059] Tables 1 and 2 compare the average electromyographic value (AEMG), integrated electromyographic value (IEMG) and root mean square value (RMS) of each muscle under "natural" force and tense force state. Among them, the time domain index is mainly used to judge the degree of muscle contraction and can reflect the degree of mobilization or recruitment of local muscle motor units. The average electromyographic value (AEMG) refers to the average value of the electrical signal generated by muscle contraction per unit time, reflecting the number of motor units activated during muscle activity, the type of motor units involved in the activity, and the degree of synchronization; the integrated electromyographic value (IEMG) refers to the absolute value integral of the electrical signal of muscle contraction per unit time, which is mostly used to evaluate the functional state and pathological condition of the muscle, and represents the total amount of muscle activity during the entire recording period; the root mean square value (RMS) is the average square root of the square value of the electromyographic signal, which is used to measure the overall strength of the signal. By comparing the above data, it can be intuitively observed that in the process of bowing, the four groups of indicators under "natural" force and tense force state of erhu players are significantly different, among which the tense force value is generally greater than the "natural" force, indicating that muscle tension under tense force state increases the degree of muscle activation. According to the electromyographic data, the force exerted by each muscle is different under different force states. When the muscle contraction force is weak, the amplitude of the electromyographic signal decreases, and the AEMG, IEMG, and RMS values ​​decrease. When the muscle strength increases, the amplitude of the electromyographic signal gradually increases, and the AEMG, IEMG, and RMS values ​​increase.

[0060] (2) By observing the spectrum of the experimental sound signal of the present invention ( Figure 6 , Figure 7 , Figure 8 , Fig. 9 ), select the bandwidth of its sound peak frequency to describe it.

[0061] The source of musical harmony is that the overtone frequency is a simple integer multiple of the fundamental frequency, and the factors that determine whether the two are in an integer multiple relationship are the simple unity of the vibrating body and the fixedness of the vibration boundary. When the sound is pure, the overtone frequency is an integer multiple of the fundamental frequency, the bandwidth of each order of overtones in the spectrum is reduced, and the background noise is less; when the sound is mixed, the overtone frequency is a non-integer multiple of the fundamental frequency, the "variation" of high-frequency overtones is particularly prominent, the bandwidth increases, the background noise increases, and the spectrum becomes messy.

[0062] from Figure 6 It can be observed intuitively that when the erhu player exerts force "naturally", the background noise in the spectrum is low, the integer multiple relationship between the overtone frequency and the fundamental frequency is obvious, the bandwidth of each overtone is narrow, and the peak values ​​are obviously distributed regularly; Figure 7 It can be observed intuitively that when the erhu player is nervous and exerts force, the background noise in the spectrum diagram increases significantly, the integer multiple relationship between the overtone frequency and the fundamental frequency is seriously destroyed, the peak values ​​of overtones of each order are randomly distributed, and the signal-to-noise ratio of the high-frequency signal is extremely low.

[0063] As shown in Table 3, when the erhu player exerts force, the 1st, 2nd, 7th, 8th, 10th, 12th, 15th, 16th, 17th, 18th, 20th, 21st, 22nd, 24th, 29th, 31st, 32nd, 33rd, 35th, 39th, 43rd, 47th, 48th, 51st, 52nd, 54th, and 55th order partials all have double peaks, and they are quite different from the standard values ​​of the partial frequencies, with the maximum value being 232Hz for the 49th order partial. When the erhu player exerts force "naturally", the majority of the order partials are single peaks, with the maximum value being 92Hz for the 54th order partial; the difference between the first 30 orders is small, ranging from 4 to 48Hz. In addition, the frequency difference between the "natural" force and the tense force partial is quite large.

[0064] Table 3

[0065]

[0066]

[0067]

[0068] from Figure 8 It can be observed intuitively that when the violinist exerts force "naturally", the background noise in the spectrum is very low, the integer multiple relationship between the overtone frequency and the fundamental frequency is very obvious, the overtone close to 20kHz, the overtone band width of each order is narrow, and the peak values ​​are obviously distributed regularly; Fig. 9 It can be observed intuitively that when the violinist is nervous and exerts force, the background noise in the spectrum diagram increases significantly, the integer multiple relationship between the overtone frequency and the fundamental frequency is obviously destroyed, the peak values ​​of overtones of each order show a random distribution, and the signal-to-noise ratio of the high-frequency signal is low.

[0069] As shown in Table 4, when the violinist exerts force under tension, the 22nd, 23rd, 25th, 28th, 29th, 32nd, 33rd, 34th, 35th, 36th, 37th, 38th, 39th, 40th, 41st, 42nd, 45th, 47th, 51st, 52nd, and 55th order partials all have double peaks, and they differ greatly from the standard values ​​of the partial frequencies, with the maximum value being 152Hz for the 42nd order partial. When the violinist exerts force "naturally", the majority of the order partials are single-peaked (except for the 46th and 57th order partials), with the maximum value being 24Hz for the 37th order partial; the difference between the first 30 order partials is small, between 0-16Hz; among them, the error of the 1st, 3rd, 10th, 22nd, 33rd, and 51st order partials is 0Hz. In addition, the frequency of the partials under "natural" force and under tension is quite different.

[0070] Table 4

[0071]

[0072]

[0073]

[0074] (3) By observing the spectrum of the experimental EEG signal of the present invention ( Fig.10 , Fig.11 ) and EEG signal frequency index comparison table (Table 5, Table 6), the frontal area (F) and central area (C) of the cerebral cortex were selected for activity.

[0075] Changes in brain wave frequency reflect the excitability and inhibition states of different brain regions. The frequency of a normal EEG is usually between 0.5-30Hz. The division of the cerebral cortex is determined according to function and structure, including the frontal lobe, parietal lobe, occipital lobe, and temporal lobe. The location of the frontal lobe is mainly responsible for "higher cognition", responsible for thinking, and plays a very important role in the preparation and execution of movement. It mainly includes two parts, the prefrontal cortex and the motor cortex. The prefrontal cortex plays an important role in planning and execution, and the motor cortex is located in front of the central sulcus and is directly related to movement. The cortical surface that controls a part of the body is related to the degree of motor control in that part.

[0076] The change of skeletal muscle force state and the change of piano sound timbre also have an impact on the activity of cerebral cortex, especially the frontal lobe and central area. The effective frequency band of 8-30Hz of EEG signal is extracted by bandpass filter, and the time domain and frequency domain characteristics of EEG signal corresponding to the electromyographic signal are obtained. Among them, it can be observed that when the performer exerts force "naturally", the C3 and C4 channel signals, which can directly reflect the changes of the performer's brain state during exercise, have a higher frequency of brain activity per unit time than when exerting force tensely. The measurement results also show that the "relaxed" force of skeletal muscle can induce brain activity and increase the discharge frequency of cerebral cortex; the tense force of skeletal muscle can reduce brain activity and significantly reduce the discharge frequency of cerebral cortex.

[0077] Table 5

[0078]

[0079] Table 6

[0080]

[0081] (4) Comparison of the electromyography, electroencephalogram, and spectrum of the erhu and violin played with “natural force” (Table 1, Table 3, Table 4, Table 6, Figure 6 , Figure 8) It can be seen that when the skeletal muscle is relaxed, the amplitude of the electromyographic signal decreases, and the AEMG, IEMG, and RMS values ​​are also relatively low; the EEG data Fp1 and Fpz are relatively high, the overtone frequency in the sound spectrum is an integer multiple of the fundamental frequency, the overtone bandwidth of each order in the spectrum is narrow, and the background noise is less.

[0082] (5) Comparison of the electromyography, electroencephalogram and spectrum of the erhu and violin played with tension and force (Table 1, Table 3, Table 4, Table 6, Figure 7 , Fig. 9 ) It can be seen that when the skeletal muscle is tense, the amplitude of the electromyographic signal increases, and the AEMG, IEMG, and RMS values ​​are also relatively high; the EEG data Fp1 and Fpz are relatively low, and the overtone frequency and the fundamental frequency in the sound spectrum are obviously non-integer multiples. The "variation" of high-frequency overtones is particularly prominent, the bandwidth is significantly increased, the background noise is also significantly increased, and the spectrum tends to be messy.

[0083] The detection method for scientific bowing of stringed instruments of the present invention is applicable to all players of stringed instruments, and its principle and detection method are universal.

[0084] The above is only a specific implementation of the present disclosure, which enables those skilled in the art to understand or implement the present disclosure. Although detailed descriptions are given with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments, and they should all be covered by the protection scope of the claims.

Claims

1. A detection system for scientific bowing of stringed instruments, characterized by: Including electromyography measurement system, electroencephalography measurement system, acoustic measurement system; The electromyography measurement system is completed by a wireless surface electromyography system to collect and measure, and the equipment includes a data receiver, a wireless surface detector, an electrode sheet, a terminal, and an electromyography collection and analysis module; the wireless surface detector and the electrode sheet are placed on the skin surface of the performer to obtain the motion electrical signal of the skeletal muscle, and then input it into the electromyography collection and analysis module to analyze the frequency characteristics; The EEG measurement system uses a wireless wearable portable EEG system for collection and measurement. The equipment includes conductive paste, electrode cap, signal amplifier, wireless adapter, terminal, collection and recording and EEG signal processing module; the electrode cap is placed on the surface of the performer's scalp to obtain the bioelectric signal released by the nerves, and the frequency characteristics can be analyzed by inputting the collection and recording and EEG signal processing module; The acoustic measurement system is completed by an acoustic and vibration measurement system for collection and measurement. The equipment includes a microphone, an amplifier, a terminal, and an acoustic collection and analysis module. The microphone is placed on both sides of the performer to obtain sound signals, which are input into the acoustic collection and analysis module to analyze their frequency characteristics.

2. A method for detecting the scientific bowing of a stringed instrument, characterized in that: include: First, the performer draws the bow with the strings open to stimulate the strings to vibrate, producing a sound of a fixed pitch that lasts for more than 20 seconds. Secondly, the wireless surface detector placed on the surface of the performer's skin collects electrical signals of muscle contraction and relaxation, the electrode cap placed on the surface of the performer's scalp collects electrical signals of brain activity, and the microphones placed on both sides of the performer collect the sound signals emitted by the bow exciting the strings and the vibration of the instrument body; Again, the electromyographic signal is transmitted to the computer acquisition module of the measurement system through the data receiver, and the electroencephalogram signal is transmitted to the signal amplifier through the wires in the electrode cap, and then transmitted from the signal amplifier to the computer acquisition module of the measurement system through the wireless adapter; finally, the electromyographic signal is generated into a spectrum diagram through the data processing module, the electroencephalogram signal is formed into a spectrum diagram through the data processing module, and the sound signal is passed through the data processing module, and the FFT signal unit is used to perform data processing and calculation to form a spectrum diagram.

3. The method for detecting the scientific bowing of a stringed instrument according to claim 2, characterized in that: The steps include: (1) Before collecting electromyographic signals, enter the collection module and follow the operating steps to connect the Bluetooth and data receiver, stick the electrode patch on the surface of the muscle to be tested, place the wireless surface detector on the patch, confirm the connection status of the detector, establish the collection module and set the collection parameters according to the muscle part to collect electromyographic signals; Before collecting EEG signals, enter the acquisition module, connect the adapter, turn on the amplifier, create the amplifier template and acquisition template in the acquisition module, set the acquisition parameters, wear the EEG cap, ensure that each lead is in full contact with the performer's scalp surface, and use a syringe to inject conductive paste into the corresponding lead port. At the same time, preview the data information and detect the electrode impedance, and set the acquisition parameters; After the two microphones are calibrated, they are placed on the left and right sides of the performer's instrument to collect the sound signal of the performer's instrument. Before collecting, the corresponding acoustic parameters are selected to determine the recording time and sampling interval; (2) The performer first uses natural gravity to push and pull the bow for more than 20 seconds, at which time the right arm is in a relaxed state. Then the performer pushes and pulls the bow in a tense state for more than 20 seconds, at which time the right arm is in a tense state. (3) At the same time, the wireless detector placed on the surface of the performer's skin collects electrical signals of muscle contraction and relaxation, the electrode cap placed on the surface of the performer's scalp collects electrical signals of brain activity, and the microphones placed on both sides of the performer collect sound signals from bowing, thereby achieving real-time collection of electromyographic signals, electroencephalographic signals and sound signals; (4) The collected electromyographic signals are transmitted to the computer acquisition and analysis module through the data receiver, and then processed to extract the time domain and frequency domain characteristics of the electromyographic signals, and analyze and evaluate the power of the human muscle during active contraction; the electromyographic signals are processed by the electromyographic analysis software to form time domain indicators and frequency domain indicators. The time domain indicators include the integrated electromyographic value, the average electromyographic value, and the root mean square value, and the frequency domain indicators include the average power frequency and the median frequency; The collected EEG signals are transmitted to the signal amplifier through the wires in the electrode cap, and then transmitted to the computer acquisition module through the wireless adapter to perform data preprocessing on these measured raw data; The collected sound signal is imported into the data processing module, and processed and calculated using the FFT signal unit to form a spectrum diagram; (5) Finally, by comparing the differences in muscles, nerve signals, and sounds when bowing under different force states, we can draw a conclusion, which can be used as data support and theoretical basis for scientific bowing judgment of string instruments.

4. The method for detecting the scientific bowing of a stringed instrument according to claim 3, characterized in that: In step (1), the electromyographic signal is collected by the BTS FreeEMG1000 device; Acquisition parameters: transmission frequency is 2.4GHz ISM band, input impedance is 100MΩ, resolution is 16bit, sampling rate is 1KHz, sensitivity is 1μV, accuracy is ±2%, RF channel is 25, acquisition frequency is 1000Hz, and EMG channel range is 3.0mV.

5. The method for detecting the scientific bowing of a stringed instrument according to claim 4, characterized in that: In step (1), EEG signals are collected by Mitsar SmartBCI device; Acquisition parameters: number of channels 24, electrode layout MCSCap21Ch, reference mode REF, storage rate 500Hz, EEG dynamic range -562.5~562.5mV, high-pass filter 0.5Hz, low-pass filter 30Hz, DC acquisition.

6. A method for detecting scientific bowing of stringed instruments according to claim 5, characterized in that: In step (1), the sound signal is collected by a Brüel&Kjær device; the recording time is 250 ms, the sampling interval is 15.26 us, the time weighting is a Hanning window, the Linear All average is used, and the overlap rate is 66.7%.

7. A method for detecting scientific bowing of stringed instruments according to claim 6, characterized in that: In step (4), data preprocessing includes importing data, filtering, deleting useless channels, reducing sampling rate, detecting artifacts, segmenting and classifying, correcting baselines, detecting peaks, and generating 3D graphics.

8. The method for detecting the scientific bowing of a stringed instrument according to claim 7, characterized in that: In step (4), the corresponding acoustic parameters are set before calculation, the FFT line number is 6400, the frequency bandwidth is 25.6KHz, and the frequency resolution is 4Hz.

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