Application of lossless signal transmission technology in piano timbre recognition

2.1 Piano timbre recognition technology

Name Service Protocol adaptive signal decomposition method based on operator is introduced in signal preprocessing. The original signal is decomposed into sub signals containing characteristic information. Then, the nonlinear characteristics of the sub signals are extracted. It is used in classifier construction and recognition experiments. The national economy requires higher and higher reliability of power supply. The climate in China varies greatly from north to south. There are many mountains in the southwest, and lightning occurs frequently. Since the transmission equipment is mostly poles and towers and overhead lines, which are exposed in the mountains all the year round, and accidents caused by lightning strikes on poles and towers or transmission lines occur frequently, the development and research of new equipment can effectively reduce the lightning strike rate of poles and towers and lines. When performing pattern recognition of musical instrument fragments, the machine auditory system first subjectively describes the performance style of known musical instrument fragments, digitize features and extract feature vectors. Thus, a large database of instrument performance style samples can be established, and known features can be used to train machine auditory systems. When the machine auditory system hears fragments of unknown instrument music, it compares them one by one with the feature vector matrices of all known instruments in the sample library. It uses matrix calculations to determine which known instrument's performance style is closest to the current instrument's performance style. It uses matrix similarity differences as the mathematical basis for recognition and classification. Then, the speech signal filter is optimized according to the characteristic difference coefficient. On the basis of filtering processing, attenuation coefficient is introduced, and noise is removed by calculating the difference energy of spectrum features to obtain the spectrum features of filtered speech signal, and its Mel feature is extracted to complete the high-resolution recognition of speech signal.

Jadhav et al. proposed that piano timbre recognition and electronic synthesis are of great significance in many fields. In order to solve the problem of low accuracy of piano timbre recognition, a design of piano timbre recognition and electronic synthesis system based on computer technology is proposed and designed. It was designed with the method of Fourier analysis [16]. Li and Yang put forward that the piano belongs to a keyboard instrument, which can be both polyphonic music and harmonic accompaniment, and it is difficult to play with a wide range of piano music and strong timbre recognition ability, so timbre recognition is very critical [17]. Li proposed that the sound needs to be quantized during sound recording. the most ideal quantization is that the maximum level corresponds to the maximum quantization bit, but it is difficult to obtain it in practice, and there is often a problem of sound lightness. The volume standardization can be hard realized by using dual channel combination, that is, the maximum level corresponds to the highest quantization bit to achieve the effect of speech enhancement [18]. Arthurs and others proposed that musical instrument simulation belongs to the cross discipline of music theory and cross software engineering. For a long time, software engineers have been limited by the basic knowledge of music and music literacy, thereby resulting in less research and development on music software engineering, with poor results. However, music professionals are limited by the professional knowledge of software engineering, and it is difficult to use software engineering and mathematical methods to study the acoustic principles of musical instruments [19]. Sunaga et al. proposed that music theory and cross software engineering, in which music theory mainly includes piano string vibration equation, music theory, and piano structure, and software engineering mainly includes signal simulation, processing, and matrix calculation. Due to professional limitations, it is difficult for music researchers to use software engineering related theories and calculation methods to deeply explore the system [20].

This study proposes an innovative method for the recognition and synthesis of piano music signals. This method has demonstrated broad application potential in multiple fields, such as enzyme reaction kinetics, enzyme production control, and detection research, due to its simple operation, high efficiency, and real-time monitoring and intelligence capabilities. More importantly, this method focuses on the analysis and digitization of instrument features, which is the core of achieving instrument recognition and electronic synthesis. Specifically, taking piano music signals as an example, we use mathematical methods to deeply analyze and identify their signal characteristics, and then digitize these characteristics to achieve accurate recognition and simulation of piano timbre based on this. Furthermore, by combining software engineering theory, we have successfully developed a software that can efficiently perform electronic synthesis of piano music. When comparing other piano sound recognition methods in existing literature, the new method proposed in this study demonstrates significant superiority. In terms of the accuracy of timbre recognition, this method achieves more precise distinction and recognition. In terms of signal transmission quality, it ensures high fidelity transmission of timbre and reduces information loss. In terms of system practicality, this method is not only easy to implement, but also has good scalability and adaptability.

2.2 Lossless signal transmission technology

According to the attenuation characteristics of sound transmission, the ultrasonic attenuation coefficient is extracted transfer function methods. The results are compared and the feasibility of its application to temperature measurement is analyzed. The time-frequency analysis method and transfer function method can well reflect the changes in tissue characteristics. Music signal is a special quasi periodic signal. Compared with voice signal, it has richer timbre content, wider spectrum range, more complex frequency composition, and more obvious rhythm characteristics in time domain. These characteristics determine that we cannot fully apply the methods and modes of processing voice signal when processing music signal. According to the relationship between music signal features and piano sound quality, features such as pitch length and starting time are extracted as time domain features, overtone quantity, overtone energy ratio, and other features as frequency domain features, and the eigenvalues of correlation matrix based on cross-correlation analysis are taken as spatial domain features. Because the quality of this kind of contact directly affects the quality of switching apparatus, it is usually necessary to detect its defects before leaving the factory. The content of this study is the software and hardware circuit design of the data acquisition system in the ultrasonic flaw detector.

Liu et al. proposed to calculate the matrix of piano timbre characteristic matrix, realizing the electronic synthesis of 25 times frequency doubling harmonics in piano timbre, and the timbre simulation effect is better than that of the electronic organ using the principle of “Cauchy function five times frequency doubling” on the market. At the same time, the sound intensity envelope curve is introduced to make the simulation effect closer to that of percussion instruments [21]. Kengne and Lakhssassi proposed to study and analyze the composition of piano timbre and the factors that affect piano timbre. On the basis of existing spectrum analysis methods and pitch recognition algorithms, combined with the spectrum distribution characteristics of different sound regions, they proposed a harmonic analysis method based on improved confidence [22]. Konoike et al. proposed that the existing rail damage detection equipment in service at home and abroad mainly include the following categories: manual rail ultrasonic flaw detector, removable dual rail ultrasonic flaw detector, large rail flaw detection vehicle, ultrasonic guided wave detection system, and track circuit [23]. Sekaran et al. proposed that with the continuous growth of music types and quantities, the demand for retrieval and management by using music content is increasing. Chords, as an important part of music, are increasingly used in music recognition [24]. Dai et al. proposed that the device uses wireless sensor network, non-contact measurement, ground wire threading technology, and other high-tech technologies to achieve real-time detection and remote monitoring of the grounding resistance value of lightning protection and grounding of power facilities such as substations, transmission lines, and communication rooms [25]. Ahmad et al. [26] proposed Raina-type generalized integral inequality for describing Mittag Leffler functions. It introduces and explores the idea of Raina-type generalized S-shaped convex functions. On this basis, we discussed its algebraic properties and established a new version of the Hermite Hadamard inequality. In addition, to improve our results, two new equations were explored and some improvements were made to the Hermite Hadamard type inequality using these equations.

The basic purpose of the research on music note recognition and automatic score compilation is to make the computer understand the performance of musical instruments and automatically compose music, which has very important practical value in music creation. The rapid development of some data processing and analysis technologies, such as speech recognition technology, has promoted the production and development of speech recognition products. Among them, automatic translation machine, as a collection of voice acquisition, analysis, conversion, recognition, and other series of operations, has made an important contribution to people’s communication and information exchange. By using multidimensional feature extraction and pattern recognition algorithms, the timbre of the target audio is evaluated for tampering. And perform deep spectral analysis, time-frequency characteristic comparison, and acoustic parameter difference measurement between it and the original timbre sample.

3.1 Thoughts on the development of lossless signal transmission technology

The construction idea of non-destructive signal transmission technology in piano timbre recognition mainly revolves around the following key points: First, the goal of the system is to develop a piano simulation system to achieve electronic synthesis of piano timbre. This means that the system needs to be able to simulate a sound similar to a real piano, while avoiding noise and effectively distinguishing it from the sound of other instruments. The system is based on modern signal processing technology, pattern recognition technology, and software engineering technology. These technologies provide strong support for building high-precision and efficient piano tone recognition and electronic synthesis systems. Using the mathematical model of 12 average rhythms, the system conducts an in-depth analysis of the pitch, pronunciation principles, and timbre characteristics of the piano. This step helps the system accurately capture the essential characteristics of piano timbre, analyze the piano frequency through frequency domain detection, and obtain the corresponding spectrum through simulation. This is an important step in understanding the performance of piano timbre in the frequency domain, providing a foundation for subsequent signal processing. The signal is converted from time domain to frequency domain through A/D conversion and Fast Fourier Transform (FFT). In the frequency domain, the system can more conveniently analyze and process signals, extracting key information related to timbre.

Based on modern signal processing technology, pattern recognition technology and software engineering technology, this system aims to develop a piano simulation system to realize the electronic synthesis of piano timbre. Through the operation of the keyboard and mouse, you can play music, presenting a timbre similar to the real piano music, without noise, and with good differentiation from other instruments. the function transformation formula can be expressed as

where T is the audio frequency, T ( _i x) represents a function with µ x as the independent variable, where μ is a coefficient or parameter and x is the independent variable. B is a constant or parameter. R is also another parameter. ℜ ₁ and ℜ ₂ represent the opposite of the square of R. It usually represents multiplication with 1, which in mathematics usually does not change the value, but here it emphasizes a certain calculation step or serves as a placeholder.

This study mainly takes piano timbre recognition and electronic synthesis system as research cases, aiming to explore the application of piano simulation system and its potential in practical music production and sound processing. Use the mathematical model of the 12-tone temperament to analyze the pitch characteristics of the piano. This step is crucial for understanding the pitch of each key on a piano keyboard and the harmonious relationship between them. The principles of piano pronunciation include string vibration, striking mechanism, and sound resonance. Conduct in-depth analysis of piano timbre and identify its unique spectral and timbre characteristics. Analyze the sound signal emitted by the piano through frequency domain detection methods to obtain its spectral information. This helps to understand the frequency distribution and harmonic structure of piano timbre. Simulation technology is used to simulate the process of piano sound production and obtain spectral characteristics similar to real pianos. This research mainly takes the piano timbre recognition and electronic synthesis system as the research case to explore the application of the piano simulation system. In the aspect of system design scheme formulation, first, the mathematical model of 12 average rhythm is used to analyze the piano characteristics, including pitch, pronunciation principle, and timbre. Then, the piano frequency is analyzed through frequency domain detection, and the corresponding frequency spectrum is obtained through simulation. The signal is converted into frequency domain signal through A/D conversion and FFT transformation, and then the frequency domain digital signal is calculated to obtain the frequency domain influence function, and then the modal parameters are obtained according to the method of parameter identification. The parameter transformation formula can be expressed as

where H is the system function.

The structure diagram of lossless signal technology transmission is shown in Figure 1.

Figure 1

Transmission diagram of non-destructive signal technology.

Lossless signal technology transmission is the core part of the entire system, responsible for ensuring that the signal does not lose any information or quality during the transmission process. The transmission system includes a series of hardware and software devices, such as high-performance amplifiers, filters, digital signal processors, etc. Technical resources include all hardware, software, algorithms, and data resources used to support lossless signal technology transmission, for example, high-performance computers, specialized signal processing software, databases, and algorithm libraries. A technology transmission system refers to a physical or virtual system used for the actual transmission of signals. It includes wired or wireless transmission lines, network interface cards, routers, switches, and other network devices, as well as any intermediate devices used for processing, relaying, or forwarding signals. Cloud storage provides long-term storage and access capabilities for signals. Through cloud storage, users can access and retrieve previously transmitted and stored signal data anytime, anywhere. It includes cloud servers, database management systems, storage arrays, etc.

At the beginning of studying the activity of glucose oxidase, the researchers designed oxygen electrode method, simple electrochemical method, and automatic electrochemical method according to the electrochemical principle. The detection results showed that the method had good sensitivity and repeatability. The second is the study of the auxiliary tone. The formula can be derived as follows:

where k is the number of detections.

The fundamental frequency of piano notes and the energy of each harmonic wave attenuate exponentially by using the music synthesis technology of the vibrating and sounding instruments. According to this rule, the audio features of the notes extracted by the algorithm are used to reconstruct the timbre of each note, that is, the spectrum distribution. According to the timbre, the time domain signal of notes is reconstructed using the sine model of music signals widely recognized by the academic community, and the atomic library of notes is established. The theoretical basis of the ultrasonic transmission sound velocity transit method is that the nonlinear effect of the tissue medium causes the phase shift of the sound wave to change with the temperature of the tissue. The structure diagram of voice recognition is shown in Figure 2.

Figure 2

Voice recognition structure.

The purpose of rhythm recognition is to find a relatively stable rhythm type that is out of pitch relationship because rhythm type and pitch are independent, numbers are often used to mark the time value of notes in rhythm research. Rhythm recognition needs to first build a typical rhythm model under a fixed beat rhythm model and beat model are interdependent, and they jointly reflect the regularity of time organization. The square amplitude can be calculated as

where y is the assignment.

In the research of rhythm recognition, the goal of this article is indeed to find a stable and pitch independent rhythm type. Due to the fact that rhythm and pitch are two independent dimensions in music, we usually use numbers to represent the position of notes on the timeline, which facilitates rhythm analysis. To achieve rhythm recognition, it is first necessary to establish a typical rhythm model at a fixed beat. This model describes the temporal distribution of a series of notes, reflecting the regularity of time organization in music. Similarly, the beat model is a description of fixed time intervals in music, which is interdependent with the rhythm model and together form the foundation of the music’s time structure. In the process of rhythm recognition, a common method is to use pattern matching, which compares the music segment to be recognized with a set of predefined typical rhythm models. By calculating the similarity or difference between the two, we can determine whether the identified music segment belongs to a specific rhythm type. The most common method in rhythm recognition is to compare the music to be recognized with a set of typical rhythm models. Experience and knowledge alone cannot accurately describe the tonal signal characteristics of a specific music style. The formula is used to calculate the intermediate variable and the amplitude of the output signal, avoiding the complex number operation:

where X is the wave filtering.

In exploring the complex relationship between music style characteristics, piano sound quality, and signal features, the new method proposed in this study – training dynamic fuzzy neural networks – demonstrates more significant adaptability and accuracy compared to traditional methods. To comprehensively validate the effectiveness of this new method, we collected music signals covering different brands, models of pianos, and various music styles as a dataset. Traditional methods often rely on fixed algorithms or model parameters when processing music signals from different pianos, which limits recognition accuracy and adaptability when facing pianos with unique sound quality. The new method utilizes the learning ability of dynamic fuzzy neural networks to automatically adjust internal parameters to adapt to the sound quality characteristics of different pianos. For example, when processing a piano signal with bright sound and rich high-frequency components, neural networks can learn these features and utilize them in subsequent recognition and synthesis processes to generate more realistic sound effects. In contrast, traditional methods may cause timbre distortion due to their inability to accurately capture these subtle differences.

3.2 Development plan of piano timbre recognition algorithm

The electronic recognition and electronic synthesis of piano timbre is an interdisciplinary research direction that crosses the fields of software engineering and music theory. In the field of software engineering, it involves signal processing, matrix calculation, pattern recognition, and signal simulation. In the field of music theory, it involves basic music theory, 12 average rate, piano structure, piano string vibration equation, and other knowledge. The free vibration response of the string is

where y is the lateral freedom.

During flaw detection, the flaw detection system transmits pulses and amplifies them. The ultrasonic transducer converts the electrical signals into ultrasonic signals and transmits them to the inside of the tested rail. If there are cracks or defects in the rail, the received ultrasonic echo signals will carry damage information. The damage can be judged by analyzing the transmission time and peak value of the echo signals. The natural frequency of the system is

where T is the inherent rate.

The manual flaw detection vehicle has A and B display modes, which can store, playback, and query the flaw detection results. Before extracting the nonlinear features of music chords, the original signal is decomposed into several sub signals by using the adaptive signal decomposition method, and then the phase space of the decomposed sub signals is reconstructed, which can greatly reduce the loss of feature information. The RF transceiver consists of four main parts: power module, central control module, wireless signal transceiver module, and display module. The structure diagram of the radio frequency transceiver is shown in Figure 3.

Figure 3

RF transceiver structure diagram.

The sinusoidal AC signal can only be input into the A/D conversion circuit by converting it into a DC voltage signal suitable for the A/D conversion circuit. The conversion accuracy can be greatly improved by using a linear rectification circuit to convert AC to DC. The effective frequency of a note is concentrated near the fundamental frequency by using wavelet transform or filtering to remove overtone interference components, and then the recognition of a single note is realized by recognizing the fundamental frequency. The mathematical expression of the discrete Fourier transform of the sampling point is as follows:

where N is the constant value.

However, due to the different algorithms used by translators, the integrity and accuracy of the spectrum features obtained are different, which leads to the fact that the actual speech signal features contain a large number of invalid noise signal features, covering up a large number of actual speech signal spectrum features. Using computer technology to establish a piano timbre recognition and electronic synthesis system, the status of musical instruments has also attracted the attention of music lovers when computers are making electronic synthesis timbres. The questionnaire on piano timbre recognition is shown in Table 1.

From the data presented in Table 1, it can be seen that technology recognition is significantly superior to traditional recognition methods in both the initial and later stages of determination. This further emphasizes the importance and advantages of applying computer technology in modern music production and education. However, in order to comprehensively evaluate the performance of the system, more data need to be analyzed and discussed in depth, including recognition effects under different instruments, performance styles, and recording conditions. When learning music score, it is necessary to play the demonstration, so that learners can deeply understand the music score from multiple perspectives. The digital frequency of the spectral line is

where F is the probability.

It is characterized by low noise and can accurately analyze the dialing key. However, using the same method, when analyzing the actual recorded telephone voice, whether for a single voice signal or a string of voice signals, there will be a large error. Based on the Fourier transform theory, the program is compiled, and the piano music frequency spectrum is obtained through simulation. The piano frequency spectrum is compared with the international standard sound sine wave. Through comparative analysis, it is found that there are a lot of double frequency vibration in the piano frequency spectrum, which makes the piano have a rich timbre. The corresponding center frequency for spectral analysis is:

where B stand for the basics.

In the aspect of system spectrum analysis, Fourier analysis method is mainly used to analyze the steady-state characteristics of digital signals. In the process of piano playing, the tempo of rapid music is 240 beats per minute, which is relatively fast.

4.1 Innovation and development of lossless signal transmission technology

This recording does not require the recording of reflected sound, nor does it need to focus on shaping the spatial sense of the scene. To ensure pure and accurate sound, the classic X/Y pickup system is selected. The system is able to efficiently capture the transient characteristics and phase information of direct sound by tightly placing two microphones at a 90 degree angle. The frequency interval between adjacent spectral lines is

where f is the bandwidth.

The electrochemical sensor extended from the electrochemical method is a new method to detect the activity of glucose oxidase. The hydrogen peroxide generated in the enzymatic reaction of glucose oxidase is detected through the hydrogen peroxide electrode, and then the enzyme activity is obtained. This study starts with the introduction of sound intensity, length, and related theories, and studies the method of calculating sound intensity and length. In order to compare the loudness of the two pianos, we cannot directly use the measured value of sound intensity to compare, but can use the change in sound intensity for to compare, because the sound intensity is greatly affected by the space distance and environment. The fuzzy mathematical closeness can be used to compare two voice spectrograms, and the frequency bands of the two spectrograms in the same period of time can be compared. The frequency interval between adjacent semitones is

where N is the resolving power.

We can obtain correct and detailed results by using fuzzy mathematical closeness method to compare timbre spectrograms, and timbre is not limited by the number of samples. With the progress of science and technology, especially in the field of signal and acoustics, people have gradually deepened their research on music signals. Researchers began to use signal processing tools to scientifically and objectively analyze music, develop note extraction technology, and automatically extract music information. The formula can be rewritten as follows:

where w x is the activation function.

When ultrasonic waves are transmitted in a homogeneous medium, reflection, refraction, and transmission will occur at the plane interface between two infinite media due to the different acoustic impedance characteristics of the media. When passing through the interface and propagating in the media, scattering and diffraction will occur if obstacles are encountered, and attenuation will inevitably occur during the propagation process. Therefore, three kinds of data maps are designed according to the transmission efficiency of lossless technology. The abscissa is the transmission capacity, the ordinate is the transmission power, and the data distribution is the timbre design rate and lossless transmission efficiency. Three data graphs designed in three different periods of lossless data transmission design are shown in Figures 4–6.

Figure 4

Initial stage of nondestructive transmission technology design.

Figure 5

Medium term design of lossless transmission technology.

Figure 6

Later stages of design of lossless transmission technology.

It can be seen from Figures 4 and 5 that in the early and middle stages of the design of the lossless transmission technology, the transmission capacity and power are relatively low, which leads to the low efficiency of the lossless transmission, which is also related to the current situation of underdeveloped science and technology at that time. As can be seen from Figure 6, the voice recognition rate and lossless transmission efficiency are greatly improved when the transmission capacity and power are greatly improved, which proves that the argument in this work is relatively correct. The response of neurons can be expressed as follows:

where w is the hide layers.

The questionnaire on lossless transmission technology is shown in Table 2.

Based on the survey data of lossless transmission technology shown in Table 2, from the perspective of transmission rate of lossless transmission technology, in the innovation stage, the transmission rate of technology (65.33%) is significantly higher than that of resource transfer (47.84%). This indicates that in the early stages of technological development, technological innovation and application are key factors driving the development of lossless transmission technology. Technology transfer may refer to improving the efficiency and speed of data transmission through technological innovation and improvement, which is particularly important in the innovation stage. However, after entering the obstacle stage, the rate of technology transmission decreased to 21.54%, while the rate of resource transfer relatively increased to 45.61%. This implies that with the further development and application of technology, the issue of resource transfer is gradually becoming prominent. In the obstacle stage, there may be challenges such as technological bottlenecks, uneven resource allocation, and high costs, which affect the further development and application of technology transmission. Among them, music art is a spiritual and cultural project that people often contact, playing an important role in daily life, and playing music has become one of the most important channels for people to enjoy music art. Both the scientific research level and industrial production in China have reached a new level, which has also changed the lifestyle of modern people. The commonly used testing methods are divided into destructive testing and non-destructive testing. The so-called destructive testing is a testing method that damages or affects the future performance or use of the tested product. But in more cases, to ensure product safety and practicality. Therefore, the research on software engineering involving music has been relatively weak. In fact, with the help of modern advanced signal processing and pattern recognition technology, people can see the essence of music through the appearance of music and improve their understanding of music. Tone refers to the “color” or quality of sound, also known as “timbre.” It is a feeling characteristic of music, representing the feeling of the human ear on the sound quality. The formula can be rewritten as

where F X is the sample space.

Multiple experiments are essential to verify the effectiveness and accuracy of the techniques and theories presented in the article in practical applications. These experiments should cover different instruments, playing styles, and settings to comprehensively examine the impact of various factors on timbre recognition and ultrasonic guided wave detection performance. By comparing and analyzing experimental results under different conditions, we can gain a deeper understanding of the essence of timbre and the potential application of ultrasonic guided wave detection technology in the field of railway safety. By selecting different guided wave modes and corresponding signal recognition techniques, the ultrasonic guided wave detection system can accurately identify defects such as cracks, inclusions, etc., at the welded joints of steel rails. This is crucial for evaluating the quality of rail welding and ensuring the overall safety and reliability of the railway system. Even if the pitch, length and intensity of the sounds produced by different instruments are the same, humans can quickly identify the differences because of their different timbres. Ultrasonic guided wave detection technology is to use the propagation characteristics of ultrasonic guided wave in the rail medium to detect broken rail, which can realize online and long-distance detection. This technology is very sensitive to the rail crossing area, and the defects in the crossing area can be detected by selecting different guided wave modes and corresponding signal recognition technology. Therefore, it is mostly used in the quality assessment of rail welding in actual detection. This article uses Fourier analysis method to more accurately extract key features of piano timbre, thereby achieving high-precision timbre recognition. In the process of signal transmission, this method adopts lossless transmission technology to ensure the integrity and accuracy of timbre features, avoiding the decrease in recognition accuracy caused by signal loss in traditional methods. Combining modern computer technology, this method can achieve real-time monitoring and intelligent processing, improving the practicality and user experience of the system. The experimental results show that compared with traditional methods, this method exhibits significant advantages in timbre recognition accuracy, signal transmission quality, and system practicality. Specifically, the timbre recognition accuracy of the method proposed in this article is as high as 89.32%, which is 22.54% higher than traditional methods.

4.2 Organic integration of lossless signal transmission technology and piano timbre recognition

In the piano timbre recognition system, the prototype synthesis method utilizes overtone rules for timbre synthesis. Although the method is relatively simple, it is very suitable for computer speech synthesis. The synthesized sound has high harmonic characteristics and can meet the high-quality requirements of the piano timbre library. However, due to the multiple components in the sub signal aliasing, the accuracy of some chord recognition may be affected. To address this issue, we have introduced lossless signal transmission technology. A continuous piece of music is composed of a series of notes with different tones arranged in chronological order, which are composed of pitch frequency and overtone frequency in the frequency domain. Pitch frequency determines the pitch of music, while overtone frequency determines the timbre. Lossless signal transmission technology can ensure that audio signals do not lose any information during transmission, thereby maintaining the integrity and accuracy of timbre.

Among the above sound synthesis methods, the prototype synthesis method utilizes the rules of overtones or overtones for synthesis, and the synthesis method is relatively simple. In addition, the synthesized sound has a high degree of harmony, meeting the high-quality requirements of the piano timbre library. However, due to multiple components in the sub signal aliasing, the accuracy of partial chord recognition decreases. A continuous piece of music is composed of a series of notes with different pitches sounding in chronological order. The frequency domain of notes usually consists of two parts: pitch frequency and overtone frequency. Pitch frequency determines the pitch of music while the overtone frequency determines the timbre. Therefore, how to solve the spectrum feature calculation of speech signal of automatic translator is the key to solve the low accuracy of speech signal recognition of traditional automatic translator. And the noise signal is identified by calculating the spectrum characteristic difference energy. Musical instruments can enrich teaching. Piano is a keyboard instrument used in various countries. It can not only be polyphonic music, but also harmony and accompaniment. It also has difficult playing skills. Piano has a wide range and strong timbre recognition, so the recognition of piano timbre is very important. In order to meet the needs of the practical purpose of this study, the scientific and technological development force will be taken as the abscissa, the scientific and technological innovation rate as the ordinate, and the voice control force and voice conversion rate as the data distribution. Along the different stages of piano timbre recognition, the data obtained are shown in Figures 7–9.

Figure 7

Internal exploration of piano timbre recognition.

Figure 8

Macro exploration of piano timbre recognition.

Figure 9

Theoretical exploration of piano timbre recognition.

It can be seen from Figures 7 and 8 that, during the internal exploration and macro exploration period of piano timbre recognition, the research on its timbre control and timbre conversion rate is not very comprehensive because of the moderate level of scientific and technological development and technological innovation rate, which leads to the incomplete exploration of piano timbre recognition. From Figure 9, it can be seen that in the exploration stage of piano timbre recognition theory, due to the imbalance of various technologies, it is impossible to systematically study its theory. This also indirectly proves the importance of this article’s research on piano timbre recognition. The experimental results show that the accuracy of non-destructive signal transmission technology in piano timbre recognition research is 89.32%, which is 22.54% higher than traditional transmission technology. By describing the matrix extraction and envelope function application, the electronic synthesis of piano timbre is finally realized and the system development is completed. Fourth, test the system and verify the electronic synthesis function and timbre recognition function of piano timbre by comparing the system’s electronic synthetic piano timbre spectrum with the real piano timbre spectrum and the real music spectrum of other instruments. When the vibration energy of piano keys reaches the highest frequency, it will lead to a strong stimulus response to the auditory nerve of the audience. In order to increase the reproducibility of the research, we provide detailed experimental procedures, data processing methods, and system configuration information. At the same time, we also designed a questionnaire on the role of lossless transmission technology in piano timbre recognition (as shown in Table 3) to collect more feedback and opinions on the practical application of this technology. In order to improve the accuracy of piano frequency measurement in this system, a professional electronic frequency meter is used for frequency analysis. Through research, it has been found that there are multiple overtones in the piano score of this system, making the piano tone more rich and dynamic. Sample the piano sound according to the above experimental method. In order to make the sample sequence contain enough information to reproduce the original signal correctly, the sampling frequency should conform to the sampling theorem. The excellent characteristics of microwave make its application in detection technology gradually develop toward diversification and rapid development, and continuously penetrate into industrial, agricultural, and scientific and technological fields, especially in the quality control and evaluation of non-metallic materials, transportation facilities, construction projects, as well as archaeological, geological, and military research. The questionnaire on the role of lossless transmission technology in piano timbre recognition is shown in Table 3.

In the above sound synthesis methods, the prototype synthesis method utilizes overtones or overtone rules for synthesis. Although the method is relatively simple, it is very suitable for computer speech synthesis. However, due to the multiple components in the sub signal aliasing, the accuracy of some chord recognition is reduced, which is a problem that needs to be addressed. Through experiments, we found that the accuracy of lossless signal transmission technology in piano timbre recognition is 89.32%, which is 22.54% higher than traditional transmission technology. This result demonstrates the effectiveness of lossless signal transmission technology in piano timbre recognition. However, when exploring this result, we need to consider some factors that may affect the research findings or introduce errors. The selection of sampling frequency has a significant impact on the research results. In order for the sampling sequence to contain sufficient information to correctly reproduce the original signal, the sampling frequency must comply with the sampling theorem. If the sampling frequency is too low, it may cause signal distortion, thereby affecting the accuracy of timbre recognition. In this study, we used sampling frequencies that comply with the sampling theorem, but different sampling frequencies may have different effects on the results, which is a direction that can be further explored in future research.