36 results about "Sequence (music)" patented technology

The invention discloses a music database retrieval method and system based on feature extraction, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the preprocessing of an audio signal of a query track input by a user, dividing the audio into equal-length frame sequences, and generating three-channel spectrogram tensors, including a Mel spectrogram, a logarithmic amplitude spectrogram and a CQT spectrogram; extracting multi-modal features of the equal-length frame sequence, dividing melody motivation segments according to pitch change and stability of the audio signal, performing differential coding, generating a melody differential sequence, and modeling the melody differential sequence into a melody topological graph; and calculating the structural similarity between the melody topological graph and a database melody graph, screening a similar melody candidate set P, mapping a melody curve into a topological manifold through a topological data analysis method, extracting persistent homology features, and converting the persistent homology features into a persistent bar graph. According to the method, the retrieval precision and the matching credibility of the complex melody in the music database are remarkably improved.

The application discloses a two-dimensional Root-MUSIC angle automatic pairing method and system based on signal receiving timing, and specific steps of the method are as follows: step 1, a Root-MUSIC algorithm mathematical model suitable for angle estimation of two orthogonal arrays of an L-shaped array is constructed, the Root-MUSIC algorithm is applied to the two orthogonal arrays respectively, and a set of azimuth angles and a set of elevation angles are estimated; step 2, timing detection and dynamic grouping are carried out based on signal arrival timing, and simultaneous multi-signal receiving processing is converted into a single-signal processing flow organized according to timing; the Root-MUSIC algorithm is executed on each group of signals, and pairing of the azimuth angles and the elevation angles is automatically completed; and step 3, the pairing result of step 2 is stored, and whether the pairing process is ended is judged, if yes, the angle pair is output in sequence. The application constructs a joint processing mechanism of timing detection-dynamic grouping-serial processing, converts traditional multi-dimensional matching calculation into a simple timing alignment operation, and makes angle pairing become a natural derivative result of the signal processing flow.

The present disclosure relates to a system and method for detecting musical infringement in symbolic music. The system receives a first musical composition and, when provided as audio, converts it into a symbolic format using a transcription neural network trained to extract a main melody. It then generates k-mer sequences comprising consecutive notes, indexes these sequences in a data structure configured for dynamic conditioning, and compares them to reference musical compositions. Upon estimating a similarity measure that exceeds a threshold, the system performs a refined local sequence alignment adapted for music, accounting for key shifts, rests, and melodic or rhythmic variations. Based on this refined alignment, the system determines whether the first musical composition includes a musical fragment that infringes upon or regurgitates a portion of at least one reference composition.

The disclosure relates to a computer-implemented method for audio generation. The method includes obtaining a first set of single-track audio data, wherein the single-track audio data is represented in a text-based single-track musical notation; obtaining a set of predefined character sequences, each predefined character sequence associated with a type of music elements in the single-track musical notation; tokenizing the first set of single-track audio data based on the set of predefined character sequences; and training a first audio generation model based at least in part on the tokenized first set of signal-track audio data. The disclosure further relates to corresponding apparatus, computer programs, and computer-readable storage media.

The application relates to the technical field of artificial intelligence-based music melody automatic generation systems, and particularly discloses an artificial intelligence-based music melody automatic generation system. The system comprises an intention analysis and feature mapping module, which is used for converting a user intention into a structured semantic feature; an emotion evolution state machine module, which is used for dynamically generating a time-series emotion state vector according to the semantic feature; a hierarchical controllable generation network module, which is used for generating a note sequence with specific styles, coherent emotions and diversity based on the semantic feature and the emotion state vector; and a post-processing and music theory constraint module, which is used for performing compliance correction and optimization on the note sequence. The application can generate a melody with coherent emotion evolution logic, high matching of a user intention, various styles and compliance with music theory.

The present application belongs to the field of program control, and particularly relates to a music fountain intelligent control method and system. The method comprises: obtaining real-time state data of a music fountain system and audio data of music to be played, framing the music audio data and extracting the composite acoustic characteristics of each frame to obtain future music characteristics in a target prediction time domain; at each control sampling time, calculating a target terminal state according to the beat strength and the harmonic feature of the future music characteristics, obtaining a terminal invariant set centered on the state as a terminal constraint; constructing a performance target function, combining the transient change rate of the future music characteristics and the harmonic entropy adjustment function to adjust the weights of the punishment water type change rate and the state deviation; online solving an optimal control problem of minimizing the target function under the terminal constraint to obtain a control sequence for driving the water pump and the valve. The present application can balance the smoothness and fluency of the waterscape and the dynamic expressiveness of the music, and improve the overall artistic effect of the music fountain.

The invention discloses a harmonic texture and sequence reasoning fused audio-to-MIDI method and system, and relates to the technical field of audio processing. Aiming at the problem of insufficient transcription accuracy of the existing polyphonic music, the method comprises the following steps: carrying out time-frequency transformation on the polyphonic music to generate a spectrogram; constructing a time-frequency target detection network, detecting notes based on harmonic comb texture features, and outputting a time-frequency bounding box; constructing a sequence inference network to extract time sequence context features; carrying out local alignment and weighted fusion on the time sequence context features by using a time-frequency bounding box by adopting an ROI-guided gating fusion mechanism; and inputting the fusion features into a decoder to generate an MIDI sequence, and correcting the note time value by using a time-frequency bounding box. According to the method, the visual texture features and the music sequence logic are effectively fused, and the time precision and robustness of polyphonic music transcription are remarkably improved.

The invention discloses a system and a method for driving hands and body postures of a human body through audio, and aims to solve the problems of difficulty in rhythm and semantic decoupling and lack of expressive force of actions in the prior art. The core of the scheme lies in a double-path parallel mapping module: one path is a rhythm-motion synchronization path which efficiently maps audio physical characteristics into a'dynamic envelope vector 'for controlling motion dynamics; the second path is an emotion-semantic coding path, and the audio content is coded into a posture prototype sequence representing a posture shape by using a depth model; and then, a cross-channel attention fusion module uses the dynamic envelope vector as a query to perform dynamic weighted fusion on the attitude prototype sequence to generate a structured attitude potential representation. Finally, the attitude decoder synthesizes the representation into a smooth, natural and highly synchronous three-dimensional human skeleton animation with the audio. According to the method, through a decoupling and cooperation mechanism, the music, diversity and reality sense of the generated attitude are remarkably improved.

The application relates to a user operation method and device by music rhythm recognition, electronic equipment and medium, and belongs to the technical field of information recognition. The method comprises the following steps: generating a demonstration melody by combination according to at least one first non-rhythm melody, a rhythm melody and at least one second non-rhythm melody arranged in sequence; generating an operation melody by combination according to the at least one first non-rhythm melody, a blank melody and the at least one second non-rhythm melody arranged in sequence, wherein the time length of the blank melody is equal to that of the rhythm melody; playing the demonstration melody and the operation melody in sequence, obtaining operation information of a blind user who imitates the rhythm melody when the blank melody is played; judging whether the operation information matches the rhythm melody, if yes, changing a parameter value of a target object; otherwise, determining that the operation information is wrong. The application has the effect that a blind user can accurately and quickly operate a target object.

The invention discloses a folk music style identification method and system and a storage medium. The folk music style identification is realized by analyzing an audio signal. Calculating a short-time energy envelope change rate to obtain a transient intensity factor, and weighting the periodic difference function by using the factor; cumulative mean normalization processing is carried out on the corrected difference function, and harmonic structure stability is calculated to screen fundamental frequency candidate points; global tonality probability distribution is calculated based on all candidate points, a state transition matrix considering a frequency difference value and tonality coordination degree is constructed, and an optimal fundamental frequency sequence is decoded; and dividing the sequence into a playing technique fragment and a melody stable fragment according to the transient intensity factor, extracting a feature subset to form a feature vector, and inputting a classification model to output a style identification result.

The application discloses a drama aria audio score recording method and system, electronic equipment and a storage medium, and relates to the field of voiceprint splicing. The method comprises the following steps: a multi-modal feature extraction step, an adversarial training score step, a hierarchical transfer learning adaptation step, a streaming processing and multi-modal verification step, and a symbol-parameter decoupling output step. The multi-modal feature extraction step comprises attention weighted fusion and dynamic adjustment of time-frequency feature contribution degree. The adversarial training score step comprises checking a symbol sequence in a score through a drama music knowledge graph. The hierarchical transfer learning adaptation step comprises adaptation training of different drama types. The streaming processing and multi-modal verification step comprises multi-modal alignment, and dynamic adjustment of the weight of model output and a rule template according to a voting result. The symbol-parameter decoupling output step comprises symbol-parameter decoupling, and output of score data in a target format.

The invention relates to the technical field of intelligent music, and discloses a piano melody deviation intelligent chord switching method for original melody of multi-style to-be-played music, and the method comprises the steps: converting to-be-played music input into a discrete note event sequence; inferring a core hypothesis including a style vector and an original melody parameter set based on the sequence, and generating a candidate accompaniment chord set; calculating a total acoustic roughness for quantifying acoustic conflicts between melody deviations caused by the original melody parameter set and standard twelve-average melody chords and adaptively selecting and outputting a final chord through a multi-objective utility function according to the total acoustic roughness and a style vector. According to the method, the acoustic conflict between the original melody deviation and the standard melody chord is quantified, and the style-adaptive decision model is applied, so that the accompaniment chord with acoustics coordination and style matching is intelligently switched out while the original melody characteristics of the melody are reserved, and the music, style adaptability and auditory sense coordination degree of automatic accompaniment can be improved.

The invention relates to a music generation method, a music generation device, electronic equipment and a storage medium. The music generation method comprises the steps that music structure information is acquired, the music structure information comprises the number of music bars of music, the number of notes included in each music bar and a chord mark corresponding to each music bar, and the chord marks represent chords corresponding to the corresponding music bars. According to the music structure information, a first music score sequence is obtained, the first music score sequence comprises a plurality of music bars, a plurality of notes included in each music bar and chord marks corresponding to each music bar, the number of the music bars is consistent with the number of the music bars, and the number of the notes corresponding to each music bar is consistent with the number of the notes; the plurality of notes corresponding to each music bar are consistent with the chords corresponding to the corresponding music bar in music expression. And converting the first music score sequence into target audio data. According to the invention, the music generation effect is more controllable.

The invention discloses a no-reference sound correction method based on a music score big language model, and relates to a mismatch song correction method in the technical field of intelligent audio processing. The objective of the invention is to solve the problems of poor pitch and rhythm collaborative correction effect, low note feature extraction precision and easy loss of emotional expression in a non-reference scene in the existing sound correction technology. The method comprises the following steps: S1, acquiring and preprocessing off-tune singing audio data; s2, carrying out note accurate segmentation on the preprocessed audio; s3, note level features are extracted, and Octuple-MIDI symbolization input is generated; s4, realizing non-reference pitch-rhythm joint error correction through a music score big language model, and outputting a standard MIDI sequence; s5, based on a standard MIDI sequence, completing cooperative accurate correction of the sound pitch and the rhythm of the song; and S6, outputting the natural corrected singing sound through a high-fidelity synthesis technology. The method is used in the field of off-tune singing processing such as karaoke entertainment, AI music creation, singing sound correction and the like.

The present invention relates to a method for generating music data on the basis of text data, an apparatus for generating music data on the basis of text data, an electronic device, a computer-readable storage medium, and a computer program product. The method comprises: on the basis of text data for describing music information or a melody, determining information for describing musical symbols of music data to be generated, wherein the information for describing musical symbols of said music data comprises at least one of the following two: a music parameter set for describing attributes of the musical symbols of said music data, and a musical symbol sequence for describing melody characteristics of said music data; and on the basis of the information for describing the musical symbols of said music data, determining music data in the form of the musical symbol sequence.

The invention relates to the field of digital music scores, in particular to a finger score generation and display method, computer equipment and a storage medium. The method comprises the following steps: obtaining a music score to be converted, and extracting the pitch, time value and playing fingering of each note from the music score; the fingering is used for indicating hands and fingers for playing notes; dividing the keyboard into a plurality of sound groups, and allocating different first display features to different sound groups; and / or, different second display features are assigned to different hand parts; based on the first display feature and / or the second display feature, according to the pitch of each note and / or the allocated playing hand, generating a frame body corresponding to each note; according to the arrangement sequence and the beat positions of the notes in the stave, the frame bodies are spatially arranged in the stave; and displaying the first identifier and the second identifier in each frame in an overlapping manner to obtain a finger score corresponding to the music score to be converted. The visual and clear mapping relation among the score surface, the fingering and the keys is realized, and the learning and playing threshold is reduced.

A method, an apparatus, a device, and a storage medium for training a model are provided. First audio content associated with vocal content is extracted from a music sample. First annotation information is generated based on the first audio content, and the first annotation information includes text content corresponding to the first audio content and first melody information of the first audio content. A first training sequence is constructed based on the text content and the first melody information. The first training sequence is input to a music generation model to generate a first set of music encoded representations. The music generation model is trained based on the first set of music encoded representations and a second set of music encoded representations of the music sample.

This invention relates to the field of artificial intelligence technology and discloses a method and system for adaptive music generation based on multimodal emotion recognition. The method includes obtaining a multidimensional emotion feature vector through sensors; determining an emotion gradient intensity sequence and direction consistency index based on the multidimensional emotion feature vector; if the emotion gradient intensity sequence and direction consistency index meet certain conditions, obtaining an emotion evolution trend vector through sliding fitting; obtaining an initial music parameter sequence based on the emotion evolution trend vector; obtaining a current feedback vector based on the initial music parameter sequence; comparing the angle between the current feedback vector and the emotion evolution trend vector; if the angle is greater than a preset angle threshold, re-planning to obtain an optimized music parameter sequence; and synthesizing an adaptive music stream audio based on the optimized music parameter sequence. This method can solve the problem of existing technologies failing to achieve natural and smooth synchronization between music and user emotions.

The invention discloses an AI music creation information processing method based on reinforcement learning, and relates to the technical field of music information processing, and the method comprises the steps: inputting a macroscopic music structure into a dynamic music knowledge map, searching an optimal element path through a map traversal and path search algorithm, and generating a symbolized action sequence in combination with rhythm-type suggestions and adapter prompts; the symbolized action sequence is decoded into a specific note sequence through a Transform decoder, smooth transition between notes is carried out in combination with an attention mechanism, and complete music data is generated according to real-time context fine tuning generation parameters; and based on the complete music data, in combination with a music theory rule, performing multi-modal music quality evaluation to generate a reward signal, and in combination with a historical hot music database, calculating style similarity and a market potential index to generate a multi-target reward function. According to the invention, intelligent selection and combination of music elements are realized, and the narrative integrating degree and professional expressive force of music are improved.

The embodiment of the invention provides a model training method and device, equipment and a storage medium. The method includes extracting first audio content associated with singing content from a music sample. Based on the first audio content, first annotation information is generated, and the first annotation information comprises text content corresponding to the first audio content and first melody information of the first audio content. And constructing a first training sequence based on the text content and the first melody information. A first training sequence is input to a music generation model to generate a first set of music coded representations. A music generation model is trained based on the first set of music encoded representations and the second set of music encoded representations of the music samples. In this way, according to the embodiment of the invention, the training sequence can be constructed through the text content and the melody content of the music sample to train the music generation model, so that the training cost of the model is reduced.

The invention discloses a virtual reality music interaction method and system based on self-adaptive rhythm detection and dynamic matching, and belongs to the technical field of computer music analysis, virtual reality interaction and audio signal processing, and the method comprises the steps of self-adaptive rhythm detection, multi-modal input fusion, dynamic rhythm matching, VR interaction presentation and feedback optimization. A personalized rhythm prompt sequence is generated through a generative adversarial network, and prompt time is optimized by combining near-end strategy optimization reinforcement learning, so that users with different response speeds and personalities can obtain good interaction experience for any fast and slow rhythms of music styles. And specifically, by dynamically adjusting the advance or delay of the prompt time of the original rhythm, the conformity of user interaction actions with different reaction speeds and personalities and the original rhythm reaches 90% or above. According to the scheme, personalized interaction is supported through dynamic rhythm matching of multi-modal fusion and AI optimization, and the limitation of original music fixed rhythm prompting is avoided.

This application provides a method and related apparatus for generating music melody, which provides diverse music theory information during the music melody generation process, thereby enabling the generation of high-quality music melodies. The method includes: acquiring a chord progression sequence, wherein the chord progression sequence includes K chords, where K is an integer greater than 1; converting the chord progression sequence into a chord vector sequence, wherein the chord vector sequence includes an encoding vector for each chord, the encoding vector of which is obtained based on the context chord encoding; inputting the chord vector sequence into a rhythm encoder to output a corresponding first rhythm vector sequence; inputting the first rhythm vector sequence and the chord vector sequence into a melody progression encoder to output a corresponding first melody vector sequence; and inputting the first rhythm vector, the chord vector sequence, and the first melody vector into a melody encoder to output a corresponding first musical melody.

This invention relates to the field of music therapy technology and discloses a method for evaluating the effect of music therapy based on physiological feedback. The method includes: acquiring music signals and physiological feedback signals, and constructing a physiological response sequence; performing structured analysis on the music signals to construct a music structure sequence; constructing a physiological state trajectory based on the physiological response sequence; performing music-physiological time-delay coupling analysis based on the music structure sequence and the physiological state trajectory to determine the coupling relationship; identifying the resonance interval between the music rhythm and the physiological rhythm, and performing steady-state convergence analysis; constructing a counterfactual baseline and performing offset analysis, while simultaneously performing multi-channel consistency analysis; and generating a music therapy effect evaluation result based on the above results. This invention, by establishing a time-delay correspondence between the music structure sequence and the physiological state trajectory, and by constructing a counterfactual baseline and performing offset analysis, distinguishes the physiological changes caused by music stimulation, thereby forming an evaluation method based on temporal correlation.

The invention provides a piano multi-modal generation method and system based on a large language model, and belongs to the technical field of artificial intelligence music generation, and the method comprises the following steps: a multi-modal coding step: coding texts, humming, images and reference tracks; a cross-modal alignment mapping step, wherein heterogeneous input is uniformly mapped to a music semantic space through an attention mechanism; an intention understanding step: analyzing a creation intention by fine tuning the large language model; a symbol sequence generation step: generating a symbol sequence containing complete playing information by adopting piano dedicated Transform autoregression; according to the method, the problems that in the prior art, an input mode is limited, intention understanding is lacked, and generation speciality is insufficient are solved, and intuitive piano creation of zero-music basic users and efficient inspiration implementation of professional creators are supported.

The present invention relates to a method for generating music data on the basis of text data, an apparatus for generating music data on the basis of text data, an electronic device, a computer-readable storage medium, and a computer program product. The method comprises: on the basis of text data for describing music information or a melody, determining information for describing musical symbols of music data to be generated, wherein the information for describing musical symbols of said music data comprises at least one of the following two: a music parameter set for describing attributes of the musical symbols of said music data, and a musical symbol sequence for describing melody characteristics of said music data; and on the basis of the information for describing the musical symbols of said music data, determining music data in the form of the musical symbol sequence.

This invention discloses an infinite music generation method and system based on bidirectional spatiotemporal sequences and natural rhythms. Using 2026 as the base year (but not limited to 2026), it constructs a bidirectional infinite sequence with positive future and negative history based on infinite non-repeating mathematical constants, ensuring the uniqueness and non-repetition of music segments from the algorithmic root. It employs a unified and standardized digital-note mapping system, simultaneously compatible with numbered musical notation, international standard pitch names (CDEFGAB), and traditional Chinese gongche notation, achieving unified pitch mapping across multiple systems. The system automatically generates music and simultaneously incorporates distributed ledger (including blockchain), rights confirmation, anti-counterfeiting, traceability, and anti-duplication verification modules. The unique ID is compatible with dark media IDs, forming a closed-loop chain from generation to rights confirmation to authorization to operation. This invention addresses the pain points of existing music generation methods, such as insufficient scalability, difficulty in rights confirmation, susceptibility to infringement, low automation, and inconsistent pitch systems. It possesses strong novelty, creativity, and industrial applicability, enabling global-scale commercial operation.

This invention belongs to the field of sentiment analysis technology, specifically relating to a method for recommending music for mood regulation based on the fusion of EEG emotional state and acoustic features of music. The method includes: obtaining discrete emotion category probabilities output by an EEG emotion recognition model and mapping them to continuous two-dimensional emotional coordinates; constructing a user emotional state vector based on the continuous two-dimensional emotional coordinates; obtaining acoustic feature vectors of each candidate music in a candidate music set; inputting historical emotional momentum sequences, temporal context encoding, user emotional state vectors, and acoustic feature vectors into a reward prediction network for processing to obtain the expected mood regulation reward of the candidate music; determining the target recommended music based on the expected mood regulation reward of the candidate music; calculating the actual mood regulation reward after playing the target recommended music to regulate mood and updating the reward prediction network online accordingly. This invention can more precisely describe the user's emotional state, more accurately select music with mood regulation potential, and continuously optimize the recommendation effect through ongoing interaction.

The application discloses a method, a data structure and a performance system for generating multi-dimensional performance control parameters based on emotional semantics, and relates to the technical field of intelligent musical instrument control. The method comprises the following steps: obtaining a to-be-performed note sequence and user emotional description information; mapping the emotional description information into an emotional feature vector through an emotional semantic encoder; obtaining a musical instrument response feature vector representing the physical response attributes of a target musical instrument; inputting the emotional feature vector, a note context feature vector and the musical instrument response feature vector into a performance parameter decoder after fusion; and simultaneously generating multi-dimensional performance control parameters for each note by the decoder, the parameters at least including a dynamics envelope parameter, a note connection smoothness parameter and a rhythm stretching and shrinking offset parameter. By taking the personalized physical response model of a musical instrument as an implicit constraint for parameter generation, the application realizes end-to-end mapping of continuous performance instructions from natural language emotional description to adaptation to specific physical characteristics of a musical instrument, and significantly improves the emotional expressiveness and cross-instrument consistency of automatic performance.

The invention discloses a piano library cloud management and intelligent recommendation system, which belongs to the technical field of digital music resource management and recommendation and comprises a music knowledge graph construction module, a mixed recommendation module, a learning sequence generation module, a cloud database module, a copyright management module, a user interaction module and a data synchronization module. By constructing a structured knowledge graph containing difficulty classification, technical characteristics and music styles, combining a mixed recommendation algorithm of content characteristics and collaborative filtering and a progressive learning sequence generation method based on the knowledge graph, intelligent organization and personalized pushing of piano teaching resources are realized by the system, and the system can be applied to the field of piano teaching. The copyright management module guarantees legal use of resources, the data synchronization module achieves efficient synchronization of the cloud and the local, the defects in the aspects of music professional feature modeling, recommendation algorithm and learning path planning in the prior art are overcome, and the teaching adaptability and learning efficiency of recommendation are remarkably improved.