67 results about "Vocal music" patented technology

The invention relates to the technical field of speech recognition, in particular to a sound model generation method and system for vocal music pronunciation training, and the method comprises the steps: collecting a preset number of sound signals of a trainer for different contents, and collecting a preset number of sound signals for each content; for any content and any modal component of any sound signal, obtaining each contrast component of the any modal component, and obtaining a feature difference array of the any modal component; obtaining a difference characteristic value between the any modal component and each contrast component thereof, obtaining an influence characteristic value and a reconstruction weight of the any modal component, and further obtaining each reconstructed sound signal; and obtaining a sound characteristic spectrum of each sound signal of the trainer, training a sound model, and evaluating the quality of new pronunciation of the trainer. The invention aims to improve the precision of the constructed vocal music pronunciation training sound model by improving the processing effect of the collected sound signals.

The construction method of the VEM-Token vocal emotion multi-modal magic modification model is different from the natural language processing model NLP-Token which interprets music through text. Instead, the VEM-Token sound-to-text innovation model comes with vocal emotion multi-modal information. The model captures and aligns the music tempo of sample songs and user learning songs, identifies various modalities of vocal emotion, divides the file into word units based on tempo, obtains VEM parameters through supervised learning and reinforcement learning, and decomposes songs into vocals, accompaniment, and emotion. The magic modification model provides a multi-modal magic modification method for imitating sample songs, including vocals, accompaniment, emotion overtones, emotion fluctuations, learning to sing, voice cloning, lyrics, pitch calibration, ornaments, tempo length, rhythm speed, tempo strength, freedom, and multiple sample magic modification. It also provides member management, mobile and PC application systems, dedicated support hardware, and communication protocols including MIDI, making it easy to access popular AI large models, reducing model hallucinations, and forming AI vocal agents and AI karaoke.

This invention discloses a breath training bottle for vocal music teaching with variable pipe diameter, belonging to the field of vocal training bottle technology. Its key technical features include a water-storage bottle body, with a support leg fixed to the lower side of one side of the water-storage bottle body. A bottle body tube is connected to the upper side of one side of the bottle body tube, with a cap attached to the upper end of the bottle body tube. A fixed tube seat is installed on one side of the upper end of the cap, and an air blowing tube is installed inside the fixed tube seat. A pipe diameter adjustment component is installed at the lower end of the air blowing tube. The pipe diameter adjustment component includes a pipe diameter adjustment end threaded onto the lower end of the air blowing tube, with connecting arms symmetrically fixed at the bottom end of the pipe diameter adjustment end. Through the cooperation of the threaded pipe diameter adjustment end and the frustum-shaped adjustment plug, the depth of the plug entering the air blowing tube can be flexibly adjusted, achieving continuous adjustment of the air outlet pipe diameter. This solves the core defect of poor adaptability in traditional fixed-diameter vocal training bottles, covering the needs of multiple scenarios such as vocal music teaching, amateur practice, and vocal cord rehabilitation.

The application discloses a natural emotional singing method and an emotional singing audio processing method, and belongs to the singing information field, and aims to solve the problems of fragmented training elements and single feedback system of a traditional vocal music teaching method. The technical scheme comprises six training modules combined by action guidance, emotion awakening and sound area training, and a training system for improving singing emotional expression capability based on a deep learning model for real-time extraction of user singing audio emotional indexes and generation of dynamic visual feedback.

A method and apparatus comprising computer code configured to cause a processor or processors to receive an audio signal obtained from a microphone, input the audio signal into frequency-domain Kalman filter (FDKF), input the audio signal and an output from the FDKF into a neural network, estimate, based on the audio signal and the output from the FDKF, and removing feedback signals from the audio signal by the neural network, recover, by a codec receiving an output from the neural network, vocal quality of a target vocal signal, and output a version of the audio signal in which the target vocal signal is enhanced by removal of the feedback signals from the audio signal by the neural network and by recovery of the vocal quality by the codec.

The application discloses an intelligent vocal music learning and assisting method and system based on artificial intelligence, and relates to the technical field of vocal music teaching.The method comprises the following steps: constructing a teacher pronunciation expression database comprising original pronunciation audio of multiple teachers, frame-level aligned real face videos and international phonetic transcription sequences, and generating teacher 3D face animation data for associated storage; acquiring learner audio data and extracting audio feature vectors; selecting the most similar target teacher based on audio feature similarity; inputting the learner audio into an audio-driven 3D face animation generation model to generate learner 3D face animation data; extracting the key frame sequences of the learner and the teacher for pronunciation actions; performing three-dimensional model difference calculation on the corresponding key frames to obtain frame-level difference quantization data, and generating visualized difference images.The application visualizes abstract vocal music pronunciation skills into intuitive 3D facial movements, realizes precise personalized guidance highly consistent with the individual characteristics of learners, and improves teaching efficiency.

The invention provides an automatic debugging method and system, electronic equipment and a storage medium. The method is used for a sound sensing device and comprises the steps that a test audio is played to a user, and the test audio is any one of words, syllables, music, voice, vocal music and pure tone; receiving response information of the user; determining whether the response information is matched with the test audio; when the response information is not matched with the test audio, storing the test audio as a reference optimized audio; determining a target distinctive speech feature corresponding to the reference optimized audio; determining a target operation parameter corresponding to the target distinctive speech feature according to a corresponding relationship between the distinctive speech feature and the operation parameter of the sound sensing device; and adjusting the target operation parameters to optimize the sound sensing equipment. The method is favorable for reducing the debugging cost and simplifying the debugging process.

1. Name of the product in this design: Vocal Practice Aid. 2. Purpose of this design: An aid for vocal practice. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

1. The name of the design product: vocal practice microphone. 2. The use of the design product: microphone for practicing vocal music. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The utility model relates to the technical field of vocal music teaching equipment, and discloses a voice recording device for vocal music teaching, which comprises a bottom plate, a voice recorder box is fixedly arranged on one side of the top end of the bottom plate and is connected with a sound pickup through a wire, and an adjusting box is fixedly arranged on the other side of the top end of the bottom plate and is connected with the sound pickup through a wire. Stand columns are slidably connected to the two sides of the top end of the adjusting box, a driving assembly for driving the stand columns to ascend and descend is arranged in the adjusting box, a cross beam is fixedly arranged at the top ends of the stand columns, and a cross rod is fixedly arranged in the middle of the cross beam; the stand column can support the cross beam, the driving assembly can control the stand column to stretch out and draw back automatically, the height of the sound pick-up is adjusted automatically through the cross beam in the stand column stretching-out and drawing-back process, operation is easy, good self-locking capacity is achieved, the stand column can be prevented from sliding off, stability is good, and safety and reliability are achieved. And supporting can be provided between the cross rod and the clamping assembly through the damping universal joint.

The invention relates to the cross technical field of computer technology and education informatization, in particular to a computer audio-visual method and system based on artificial intelligence and a computer readable medium, which are used for remote vocal music, dance or fitness action teaching. Image and audio data are converted into computable skeleton sequences and feature vectors through artificial intelligence, and are compared with standard data, so that the defects that traditional teaching depends on subjective experience of teachers, and real-time quantitative feedback cannot be realized are overcome. By constructing the unified virtual room and mapping all trainees into virtual characters therein, physical space isolation is broken, remote trainees can obtain the immediacy sense and team sense of'same-field training ', and learning motivation and interactivity are greatly improved. The evaluation result is instantly converted into visual elements (such as scores and special effects) in a virtual scene, visual and instant positive feedback or error correction prompts are provided for students, the skill acquisition process is accelerated, and a set of computer audio-visual method based on artificial intelligence is formed.

This application provides a vocal practice assistance system based on vocal cord fatigue analysis, comprising: a surface-mounted electrode for attaching to a detection target to acquire electromyographic (EMG) signals; an EMG signal acquisition module for acquiring the EMG signals transmitted by the surface-mounted electrode and performing analog-to-digital conversion; a data transmission and reception module for sending the EMG signals acquired by the EMG signal acquisition module to a host computer and receiving relevant information; a preprocessing module for preprocessing the EMG signals transmitted by the data transmission and reception module to reduce noise; a signal separation module for separating the preprocessed EMG signals; a feature extraction module for acquiring the time-domain and frequency-domain features of the EMG signals; and a fatigue analysis module for linearly fitting the time-domain and frequency-domain feature curves and analyzing the vocal cord fatigue trend through the slope of the linear fitting equation. This system can monitor and analyze the degree of vocal cord fatigue in real time and provide a convenient and reliable vocal practice assistance device for vocal professionals.

The present application relates to a kind of vocal performance scoring method and system based on neural network and audio-visual fusion, belong to vocal evaluation field.The method utilizes different neural networks to obtain expert score data in three dimensions, including audio score, emotion score and dressing score, then the scores in three dimensions are input into expert score fitting neural network, and finally the comprehensive score is obtained.The present application makes the evaluation result more real and effective, close to expert score, and the scoring process is more efficient and convenient.

This invention proposes an intelligent error correction method and system for ethnic vocal music dialect prosody. The method includes: collecting multimodal data, extracting features to form a training dataset, constructing and updating a dynamic dialect prosody map, and performing cross-modal comparative analysis to obtain a joint representation of cross-modal error features. A neural vocoder direct-connect correction model is constructed, inputting the joint representation to generate a corrected Mel spectrum. A cross-modal generative adversarial network is constructed, including a generator and three discriminators. The generator generates a joint latent representation based on high-fidelity corrected audio and three features. The three discriminators respectively judge the naturalness of the audio, the compliance of the text tone, and the rhythmic coordination of the musical score. This invention solves the problem of manual error correction by constructing a prosody map, overcomes the bottleneck of manual detection by utilizing cross-modal comparative analysis, improves the generalization ability of the model by combining meta-learning algorithms, and optimizes the correction results with the help of adversarial networks, thereby reducing manual costs and improving the error correction efficiency of ethnic vocal music works.

The invention relates to the technical field of remote education, in particular to a remote vocal music teaching multi-angle image synchronous analysis system. Comprising a student image data acquisition module, a course database, a curved surface manifold construction module, a geodesic deviation analysis module, a multi-modal interaction feedback module, an audio acquisition module, a pitch-attitude correlation analysis module, a teacher remote guidance module and the like. The difference between the student attitude and the standard attitude is accurately quantified through a geodesic deviation vector field, an optimal attitude correction path is calculated based on curvature optimization, real-time guidance is provided through multi-mode feedback of vision, hearing, touch and the like, the system further establishes association mapping, auxiliary analysis and guidance of acoustic features such as pitch and tone and attitude manifold features, and the system is applied to the field of teaching. Through the edge calculation and adaptive bandwidth management technology, the delay problem in remote teaching is solved.

The invention discloses a nasal sound recognition method based on acoustic feature decoupling, which is characterized in that a double-flow decoupling deep neural network is constructed, a mutual information minimization adversarial training strategy is introduced, and sound source features (vocal cord vibration) and sound channel features (oral cavity and nasal cavity adjustment) are forcibly separated from a single audio signal. On the basis, the high-resolution characteristic of CQT is used for accurately extracting acoustic characteristics related to the nasal sound, physiological offset correction is carried out in combination with personal acoustic fingerprints, and finally accurate quantification and recognition of different nasal sound states (normal, defect and skill) are achieved, so that an objective and visual feedback basis with artistic style adaptability is provided for vocal music teaching.

A metaverse personalized digital singer generation system and a method thereof. In the system, the server-end device receives a user voice, store the user voice as a personalized voice, capture an image of a user face to generate a facial image, generate a personalized digital singer displayed in a virtual scene through a 3D imaging technology, and convert the personalized voice into voice feature vectors, and use the voice feature vectors and the personalized voice as training data, input the training data to a generative AI model to train a generative pre-training model having the personal characteristics. When the user selects an original song for singing, the original song and the user singing voice and the prompt are inputted to the generative pre-training model, the remixed song matching a style of the original song is outputted, a vocal coaching is generated and displayed based on prompt.

The application relates to the technical field of voice recognition, in particular to a sound model generation method and system for vocal singing pronunciation training, which comprises the following steps: collecting sound signals of a trainer for a preset number of different contents, and collecting a preset number of sound signals for each content; for any modal component of any content and any sound signal, acquiring each contrast component of the any modal component, and acquiring a feature difference array of the any modal component; acquiring a difference feature value between the any modal component and each contrast component thereof, acquiring an influence feature value and a reconstruction weight of the any modal component, and then obtaining each reconstructed sound signal; obtaining a sound feature map of each sound signal of the trainer, training a sound model, and evaluating the quality of a new pronunciation of the trainer. The application aims to improve the processing effect of the collected sound signals and improve the precision of the constructed vocal singing pronunciation training sound model.

The invention provides a human voice customization simulation and song generation method and system. The method is applied to the technical field of audio processing, and comprises the following steps: acquiring vocal music data, vocal music texts and evaluation indexes of different singers as a sample set; performing feature extraction on the preprocessed sample set to obtain four-image features and context features; the four-image features and the context features are utilized to train a four-image mapping model, the four-image mapping model is used for outputting acoustic signals based on the incidence relation among the four-image features, the four-image mapping model comprises an input layer, an association layer and an output layer, and the association layer comprises a forward association module and a reverse feedback module; and inputting to-be-processed vocal music data of a target singer into the trained four-image mapping model, and outputting target human voice. The method has the advantages that the generated human voice music is not only matched with the personality of the target tone, but also highly synchronized with lyric emotions, tunes and the like, the reverse feedback module is arranged, the deviation is corrected in real time, and it is ensured that the output conforms to the physical law and meets the aesthetic requirement.

The application discloses a vocal music teaching feedback optimization method based on intelligent voice, which comprises the following steps: S1, collecting vocal music voice data and performing pretreatment; S2, performing frame-level path matching, calculating a difference vector and performing window aggregation, and constructing an error feature matrix; S3, performing multi-scale convolution aggregation and cross-frame dependent propagation based on the error feature matrix, continuously performing regression on the aggregation result, and updating regression parameters; S4, continuously dividing and type-aggregating the error feature matrix, extracting error statistics and scoring change trend, and constructing a teaching decision space; S5, performing action value scoring on error features of each segment in the teaching feedback decision space, and performing action sampling, and screening the sampling result; and S6, collecting feedback data, and updating strategy value estimation parameters. The application can realize frame-level fine positioning of vocal music singing errors, and improve the accuracy of teaching feedback.

A system and method leveraging artificial intelligence to modify song lyrics while preserving the unique vocal characteristics of the original artist, including pitch, timbre, and expressive style. The system processes an input music file, separates vocal and instrumental components, and uses a trained singing voice model to synthesize a modified vocal track that seamlessly integrates new lyrics. The output retains the original artist's distinct vocal qualities, ensuring a realistic and natural rendition. Applications include content localization, personalized music production, and media streaming, enabling efficient lyric customization without requiring re-recording by the artist.

1. The name of the design product: cup water cup. 2. The use of the design product: for vocal music teaching. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective drawing.

The singing table comprises a singing table frame, the outer side of the singing table frame is fixedly connected with a stair frame, a control device is arranged in the stair frame, the top of the singing table frame is provided with a first containing groove, a lifting table is connected in the first containing groove in a nested and sliding mode, and the lifting table is provided with a lifting handle. A first storage groove is formed in the top of the singing rack, a first groove is formed in the inner wall of the first storage groove, a first screw rod is rotationally connected into the first groove, a first screw block sleeves the first screw rod in a threaded mode, the first screw block is fixedly connected with the lifting table, a first motor with the output end fixedly connected with the top end of the first screw rod is arranged in the singing rack, and a second storage groove is formed in the top of the lifting table. A fence frame is slidably connected into the second storage groove in an embedded mode, and a lifting adjusting mechanism is arranged in the second storage groove. The whole structure is stable in connection, simple and convenient to operate, high in adjustability and flexible and convenient to use, and compared with a traditional singing table with a single fixing form, the stage effect of vocal singing is better.

The utility model discloses a microphone stand for a vocal music stage, and particularly relates to the technical field of vocal music equipment, the microphone stand comprises a base and a support rod arranged in the base and used for adjusting the height of a microphone, the outer surface of the base is slidably connected with a driving plate, and the bottom of the outer side surface of the base is hinged with three storage arms; and three driving rods are hinged to one side of the outer surface of the driving plate, the three driving rods correspond to the three storage arms one to one, and the ends, away from the driving plate, of the three driving rods are hinged to the three storage arms correspondingly. According to the microphone stand for the vocal music stage, in actual work, a first rotating handle is held and rotated to enable a first threaded rod to rotate, so that a driving plate can ascend, three driving rods can drive three storage arms to rotate towards the direction of the base, a second rotating handle is held and rotated to drive a second threaded rod to rotate, and the first threaded rod is rotated to enable the driving plate to ascend; and the supporting rod descends to the inner bottom of the base, so that storage is facilitated, the occupied space in the carrying process is reduced, and carrying is facilitated.

The utility model relates to the technical field of vocal breath practicing, and discloses a vocal breath practicing device which comprises an air inlet pipe, an air nozzle is fixedly connected to the left side of the air inlet pipe, an isolation sealing mechanism is arranged in the air inlet pipe, a transparent pipe is slidably connected to the interior of the air inlet pipe, and a scale strip is arranged on the front face of the transparent pipe. A shielding mechanism is arranged at the top of the transparent pipe, a clamping column is slidably connected to the interior of the air inlet pipe, and a first spring is fixedly connected between the clamping column and the air inlet pipe. Through the arrangement of the isolation sealing mechanism, when the device is not used, the shielding face of the shielding plate is parallel to the air inlet by resetting the shielding plate, and meanwhile the shielding plate is reset to enable the shielding face to be parallel to the air inlet; the shielding plate shields the outlet of the transparent pipe, so that external dust and small particles are prevented from entering the air inlet pipe of the transparent pipe when the device is not used and is carried daily, a large amount of dust is prevented from blocking the transparent pipe and the air inlet pipe, and daily use of the device is prevented from being affected.

The invention belongs to the technical field of vocal music training and muscular fascia rehabilitation, and discloses a facial fascia release training method and device for recovering high pitch ability, which comprises a fascia pen, a penholder, an acupoint pressing bead and a shovel blade head and comprises the following steps: dynamic looping probing release, static pressing fascia release, superficial fascia shearing separation and sound production function consolidation training. Through functional division of the acupoint pressing bead and the shovel blade head, and by matching with definite pressure intensity and displacement amplitude, stepped release of deep exploration, fixed-point release and superficial layer shearing is realized, and the multifunctional acupoint pressing shovel can accurately act on musculus such as masseter, intrawing muscle, extrawing muscle, hyoid superior and subordinate muscle groups and sternoclavicular mastoid muscle as well as key parts such as fascia intersections, so that the operation is simple and convenient. The fascia adhesion problem causing high pitch difficulty is directly solved, and the defect that in traditional training, only vocal cord functions are practiced, sound production habits are corrected, and sound production obstacle roots are not solved from the physical level is overcome; the shovel blade head adopts the smooth arc-shaped cutting edge design, the acupoint pressing bead is round and free of edges and corners, the whole process is non-invasive operation in cooperation with scientifically controlled pressure intensity and sliding amplitude, damage to skin, muscles or vocal cords cannot be caused, and the shovel blade head is suitable for crowds with high pitch increasing requirements such as vocal music learners and occupational singers and also suitable for people with high pitch increasing requirements. And the device is also suitable for common users who have difficulty in sound production due to improper long-term sound use and muscular tension, can perform preliminary operation without professional guidance, and is beneficial to practical application and popularization.

The application provides a kind of antenatal education interaction method based on vocal singing feedback training interface, the method is based on the user brain wave data set, the activity of different frequency bands is analyzed, the intensity of each wave band is identified and quantified, and the current emotional state of the user is judged by comparing with the preset threshold, and the time point of emotional change is recorded, the emotional analysis result is obtained, the rhythm, volume and spatial sound effect setting of music are adjusted, the dynamic adjustment of music output parameter is realized, the emotional intervention demand is optimized, and according to the change of user brain wave, the emotional improvement and user satisfaction are evaluated, the improvement effect of music intervention on user psychological state can be measured, the effectiveness of feedback closed loop is ensured, and more efficient, accurate and psychological demand meeting music feedback training effect is provided for user.

The method of VEM-Token beat capture and alignment model construction is based on the VEM-Token vocal emotion multi-modal model method, which uses musical beats to further innovate the division of vocal files into VEM-Token units. The core of this method is to establish a beat model, a beat capture model, and a beat alignment model for vocal files. The former separates the sample vocal file into singing, accompaniment, and emotional fluctuations through multiple filters, captures the start and end points of the beat in the frequency spectrum format file, and the latter uses start and end point fine-tuning models to align the user's imitation file with the sample file. The beat is captured using models including beat base model, harmonic impact, joint learning, harmonic frequency layering, dynamic time warping, etc. The construction of models such as base model, start point fine-tuning and end point fine-tuning, full alignment verification, beat editor, free beat, repeat alignment, and communication interface protocol makes this method suitable for Agent music intelligent agents and AI music applications.

An object of the system according to the embodiment is to consistently perform lyrics writing / composition, voice training, and production based on personal information.SOLUTION: A system includes an information collection part, an analysis part, a lyric composition part, a voice training part, a production part, and a debut support part. The information collection unit collects information on at least one of a user's daily conversation, SNS, and diary. The analysis unit analyzes the information collected by the information collection unit. The lyrics composer performs lyrics composition based on the information analyzed by the analyzer. The voice training unit performs voice training based on the lyrics and the melody generated by the lyrics composing unit. The producing unit produces the performance of the user trained by the voice training unit. The debut supporting unit supports debut of the user produced by the producing unit.SELECTED DRAWING: Figure 1

An air circulation system for vocal music and band ensembles provides a downdraft through a riser structure to remove aerosols from around performers in the riser structure. The riser structure includes platforms and an air intake disposed within a vertical space between the platforms. The aerosols are drawn through the riser structure, cleaned, and filtered and exhausted away from the performers.