76 results about "Chord (music)" patented technology

A method for generating a musical score, a device, and a computer-readable storage medium. The method includes: obtaining target audio; generating a chromagram of the target audio corresponding to each pitch class, utilizing the chromagram to identify a chord of the target audio, and obtaining chord information; performing mode detection on the target audio, and obtaining original key information; performing rhythm detection on the target audio and obtaining the beats per minute; performing identification on a beat type of each audio frame of the target audio, and determining an audio time signature on the basis of a correspondence relationship between a beat type and a time signature; utilizing the chord information, the original key information, the beats per minute, and the audio time signature and performing musical score rendering, and obtaining a target musical score.

The invention relates to the field of neuroscience and music therapy, and discloses an emotional chord music generation method and system based on EEG signals, and the method comprises the following steps: selecting an area which is located in the brain and is related to basic emotional processing, emotional regulation and cognitive processing as an EEG electrode position; acquiring a signal of the position of the EEG electrode, and preprocessing the signal; classifying the EEG signals by using a machine learning algorithm, and identifying different emotional states; constructing an emotional state-music parameter mapping multi-dimensional model; and mapping the identified emotional state to a music parameter conforming to the harmony of the trichord so as to generate music matched with the emotional state. According to the method, the EEG signal and music generation are creatively combined, the trichord music which is matched with the emotional state and has a harmonious pitch relation is generated according to the emotional state, and the individuation and accuracy of music generation are improved.

The invention relates to the technical field of music information retrieval, in particular to an automatic classification method and system for music works, and the method comprises the following steps: based on the classification standard of music genre, analyzing the hierarchical node relationship of the music genre, extracting the corresponding hierarchical characteristics of the large class and the small class of the genre, identifying the association proportion between each hierarchy, and analyzing the hierarchical connection strength. And generating a music genre level weight table. According to the method, through deep analysis of a music genre hierarchical structure, finer and dynamic classification of music works is realized, the classification accuracy and flexibility are significantly improved, through extraction of hierarchical features and revelation of association between hierarchies, music classification not only breaks through limitation of simple labeling, but also can reflect hierarchical relationships between genre, and the classification efficiency is improved. The chord change rule and amplitude are extracted, the dynamic capture of music features is enhanced, the association of chord change and genre level features is matched, the classification weight is dynamically adjusted, genre features are accurately matched, and the diversity and complexity of music creation are better adapted.

The present application discloses a method, device, equipment, storage medium and program product for generating music accompaniment, and relates to the field of computer technology. The method comprises: obtaining music data; extracting music information from the music data to obtain beat data information, chord data information and melody data information; using the melody data information as a generation restriction condition for the music accompaniment, and generating music accompaniment data through the beat data information and chord data information; rendering audio data based on the music accompaniment data to obtain the music accompaniment corresponding to the music data. In the above manner, it is possible to quantize the music beat and music notes, incorporate the beat and chord into the accompaniment melody generation process within the limitation of the music melody, so that the music accompaniment data can be presented more meticulously through music parameters, thereby improving the generation stability and generation effect of the music accompaniment. The present application can be applied to various scenarios such as music accompaniment generation.

A Chord Map comprises three circular, independently rotatable, concentrically aligned layers of music information: chord array, mode numerals, and grouping. The chord array layer comprises an inner ring (12 major chords), a middle ring (12 minor chords), and an outer ring (12 diminished chords). Text, color, and shape are used to identify the chords, harmonic relationships, and intervallic distances. Spatial organization illustrates chord relationships. The mode numerals layer comprises an inner ring, a middle ring, and an outer ring—each comprising 12 symbols to identify major chords, minor chords, and diminished chords. Sharp and flat symbols further distinguish chords in relation to a given tonic. The mode numerals layer may additionally comprise seven mode labels. The grouping layer comprises an outline to group chords and symbols in the other two layers. The mode numerals layer and grouping layer are transparent so that the information in the chord array layer is visible.

The invention provides a controllable generation method and system for symbolic music, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the preprocessing of the pre-obtained music content and annotation information through a preset representation module, so as to form an original data set; performing masking processing on the original data set according to a preset chord condition rule to form an original masking data pair, and sending the original masking data pair into a preset music discrete diffusion framework for repeated training to form a music discrete diffusion model; and then performing preorder processing on the music frequency band and the music demand input by the user to form a music content representation and condition matrix, inputting the music content representation and condition matrix into the music discrete diffusion model, and enabling the music discrete diffusion model to perform music generation so as to output a target music score. The richness of music controllable generation can be realized, and the reliability and rationality can be improved.

To provide a guitar playing assistance system that enables even a beginner to play the guitar easily. [Solution] A guitar playing assistance system AS that assists in playing the guitar includes a plurality of colored stickers, each with a different color for each chord, that indicate on the fingerboard the position of the fingers that should press down on the strings for each chord, and a musical score display medium 400 that has a plurality of colored marks 310, 320, 330 arranged in the order of the chord progression, each having the color of the colored sticker that corresponds to the chord. With this guitar playing assistance system, the player can intuitively play the guitar by pressing down on the strings with their fingers in the order of the colored marks, at the positions of the colored stickers that match the colored marks.

Systems and methods are described for music notation level generation that can translate commonly used music notation into information that can be used within a game engine to create music based levels and games; an audio recognition engine capable of detecting individual pitches, chord tones, and multi-note sounds all simultaneously and in real-time, for game interactions, as well as identifying and isolating individual players' audio in order to create a real-time collaborative gaming experience; real-time audio to music notation parsing that can utilize real-time pitch detection and internal timing plus metronome features in the audio engine to hear what a player is playing on their instrument, then translate that to sheet notation and further into configuration files facilitating instant user-created levels in game; and machine-learning based content generation to generate personalized in-game content for players, for instance through the use of large language models.

Embodiments of the present application disclose an audio processing method, a model training method and device, equipment, a storage medium and a product. The audio processing method comprises: in the embodiments of the present application, obtaining a rhythm condition vector corresponding to a motion video to be matched with music and reference chords of the motion video, performing beat dimension noise reduction processing on noise data based on the rhythm condition vector to obtain a predicted beat of the motion video, performing chord dimension noise reduction processing on the noise data based on the rhythm condition vector and the reference chords to obtain a predicted chord of the motion video, and fusing the predicted beat and the predicted chord to obtain music matched with the motion video. It can be seen that the beats and chords of the music are respectively subjected to noise reduction processing through different constraint conditions, the prediction process of the music can be decoupled, the prediction process of the beats and chords is more targeted, and thus the quality of the music matched with the motion video is improved.

The present disclosure provides a computer-implemented method for adapting a reference playback to a user-submitted recording. The method comprises receiving a user-uploaded recording comprising vocals and at least one harmonic instrument, selecting or receiving a reference style input, and analyzing the uploaded recording to determine musical structure including division into bars. The method further comprises selecting or generating a multi-track reference playback based on stylistic similarity to the reference style input, segmenting each instrumental track of the multi-track reference playback into bars aligned with the musical structure of the uploaded recording, and performing bar-by-bar harmonic retargeting of the segmented instrumental tracks. The retargeting comprises transposing notes and chords in each bar to conform to harmonic content of the uploaded recording. The method concludes with outputting an adapted recording comprising the harmonically retargeted instrumental tracks and the vocals from the uploaded recording.

The present invention relates to the field of music information retrieval technology, specifically to a method and system for automatically classifying music works, comprising the following steps: based on the classification standard of music genres, analyzing the hierarchical node relationship of music genres, extracting the corresponding hierarchical characteristics of genre major categories and minor categories, identifying the correlation ratio between each level, analyzing the hierarchical connection strength, and generating a music genre hierarchical weight table. In the present invention, through the in-depth analysis of the hierarchical structure of music genres, a more refined and dynamic classification of music works is achieved, and the accuracy and flexibility of classification are significantly improved. By extracting hierarchical features and revealing the association between levels, music classification not only breaks through the limitations of simple labeling, but also reflects the hierarchical relationship between genres, extracts the law and amplitude of chord changes, enhances the dynamic capture of music features, matches the association between chord changes and genre hierarchical features, dynamically adjusts the classification weight, accurately matches genre characteristics, and better adapts to the diversity and complexity of music creation.

The utility model discloses a 13-hole Bloom chord harmonica, which is formed by assembling a comb, an upper spring plate, a lower spring plate, a cover plate and a bottom plate, the comb is provided with 13 holes, the air blowing tones of the comb are respectively 5, b7, 2, 4, 6 and 1 from left to right, and the inspiration scales of the comb are respectively 6, 1, 3, 5, 7, 2, 4, 6, 1, 3, 5, b7 and 2 from left to right. The musical instrument is small in size, rich in chord tone, capable of playing thirteen kinds of chords, thirteen kinds of chords, more than twenty kinds of chords, more than twenty kinds of chords, more than twenty kinds of chords, more than twenty kinds of chords, more than twenty kinds of chords, more than twenty kinds of chords and more than twenty kinds of chords and more than twenty kinds of chords, and capable of playing diphonic ranges of various degrees through double holes and tongue plugs

A musical instrument includes a chord player portion having at least one chord player operable to play a selected chord, the at least one chord player including a finger actuatable first pad having a first center point function and a plurality of first peripheral point functions. The first center point function and the plurality of first peripheral point functions are each operable to generate a major chord having a chord root tone or a variation of a major chord having a chord root tone.

An information processing device includes a processor that controls to advance a syllable corresponding to a voice to be sounded from a first syllable to a second syllable, which is a next syllable, in a case where an operation on an operation element is determined to be an operation of specifying a chord, and controls not to advance the syllable from the first syllable to the second syllable in a case where the operation on the operation element is determined not to be the operation of specifying the chord.

This application discloses a method for generating musical audio, a computer device, and a computer storage medium. Based on a currently set musical scene, chord progressions and musical structures are generated. According to the musical characteristics represented by each melodic fragment, motivic melodic fragments are determined from each melodic fragment. These motivic melodic fragments serve as musical motifs corresponding to the musical scene. The motivic melodic fragments are arranged according to the musical structure to reproduce the motivic motif, resulting in a sequence of melodic fragments. At least one interpolated melodic fragment is added to the sequence of melodic fragments based on chord progressions to develop the motivic motif, resulting in a musical audio file that matches the musical scene. The arrangement of the music can be flexibly changed according to the user's listening scenario and needs. The user only needs to set the musical scene, without needing to change the music arrangement template or understand the technical terminology of music arrangement. This reduces the time spent on music generation and improves the efficiency of music generation.

A method of generating an audio output file via a computer system includes receiving one or more audio files including musical tracks; separating each audio file into at least one selectable audio block; selecting an audio block and analysing the harmonic chord structure of the musical track; determining a new harmonic chord structure; and adapting the musical track to the new harmonic chord structure. The system for outputting an audio file includes a unique identifier module; a chord progression constructor; an instrument role allocator; a melodic DNA transposer; a musical elements analyst; an element selector module; an integration & adaptation module; a genre application module; an integration & adaptation module; and a genre application module. The system includes an interactive user editing control panel, aimed at offering extensive control over various musical aspects within an AI generated music track. The output file can be an audio file or MIDI file.

The application discloses a kind of electronic organ dynamic music score generation and keyboard highlight guiding method and system, method includes receiving user input music and difficulty parameter, and pre-processing;Adopt the multi-audio track separation and chord inference algorithm based on deep learning, extract the music features including melody, chord and rhythm in pre-processed music;Adopt multi-modal Transformer model, generate dynamic electronic organ music score according to the music features and difficulty parameter, and adjust music score content according to note density and rhythm complexity;According to electronic organ music score, the keyboard that needs to be played is indicated using light system on electronic organ;Through the collection user's keying speed and accuracy, evaluate user skill level, and adjust electronic organ music score difficulty according to evaluation result in real time;Adopt music generation Transformer technology to generate accompaniment note sequence, and provide a variety of accompaniment instrument options and style adjustment options according to user preference.The application can realize full-process automation and personalized learning.

Embodiments of the present disclosure provide an audio processing method and apparatus, an electronic device and a storage medium, wherein the method comprises: obtaining first music data and a processing instruction in text form associated with the first music data; extracting, by a music processing model, a first chord progression feature and an audio feature of the first music data, and a text feature of the processing instruction; processing, by the music processing model, the audio feature in accordance with the first chord progression feature and the text feature, to generate second music data; wherein a similarity between a first chord progression feature of the first music data and a second chord progression feature of the second music data is greater than a similarity threshold.

The application relates to the biomedical technology field, in particular to a brain electrical power spectrum density feature extraction method based on music tonality function attributes, which comprises the following steps: acquiring brain electrical signals collected under long-section natural music stimulation; after pre-processing the brain electrical signals to remove noise and artifacts, performing window slicing operation on the pre-processed brain electrical signals based on tonality function attributes to obtain multiple brain electrical signal segments; extracting power spectrum density features in each brain electrical signal segment through a time-frequency analysis tool; and analyzing differences in brain electrical responses induced by different tonality function attributes based on the extracted power spectrum density features, so as to determine the weight of each tonality function attribute in the brain electrical signals. The method can solve the technical problem of the defect that the existing technology cannot quantize the weight of functions and chords in long-section natural music.

A user interface for a musical instrument, particularly an electronic or virtual instrument, is proposed. The instrument is used to play combinations of chord and melody sequences. The user interface has a key array 1 with multiple operable, row-and-column arranged areas 111, 121, ...; 112, 122, ...; 11 n 12 n ..., each row has operable regions 111, 121, ...; 112, 122, ...; 11 n 12 n ...forming a block 101, 102, ..., each block 101, 102, ... is equipped with a basic chord, preferably a scale, preferably a whole-tone scale-specific chord; each section 111, 121, ...; 112, 122, ...; 11 n 12 n ...equipped with a melodic tone, preferably a scale melody tone, the user interface is configured for use in the manipulation blocks 101, 102, ..., areas 111, 121, ...; 112, 122, ...; 11... n 12 n ..., corresponding to the manipulated regions 111, 121, ...; 112, 122, ...; 11 n 12 n ... and blocks 101, 102, ... generate pitch generation instructions, which include at least one basic chord note instruction with pitch and a melody note instruction, the pitch of which is contained in the basic chord assigned to the manipulated blocks 101, 102, ..., and the pitch of the melody note instruction corresponds to the manipulated blocks 111, 121, ...; 112, 122, ...; 11 n 12 n The melodic tones of... are also proposed. Furthermore, an instrument, a method for generating combined chord and melody sequences, and a computer-readable storage medium are also proposed.

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.

A music accompaniment generation method and apparatus, a device, a storage medium and a program product, relating to the technical field of computers. The method comprises: acquiring music data (210); extracting beat data information, chord data information and melody data information from the music data (220); generating music accompaniment data on the basis of the melody data information, the beat data information and the chord data information (230); and executing audio data rendering on the basis of the music accompaniment data to obtain a music accompaniment corresponding to the music data (240). In this way, music beats and music notes can be quantized, and the beats and chords are incorporated into an accompaniment melody generation process within the limitation of the music melody, so that music accompaniment data can be presented more precisely by means of music parameters, thereby improving the generation stability and the generation effect of music accompaniments. The present application can be applied to various scenarios such as music accompaniment generation.

The application discloses an audio data processing method, a computer device and a storage medium. The method can be applied to various scenes such as cloud technology and artificial intelligence. The method comprises the following steps: performing feature extraction on to-be-processed audio data to obtain feature information of each audio segment included in the to-be-processed audio data, wherein the feature information of each audio segment comprises one or more of chord information, pitch information and envelope information; then performing relaxation processing on the feature information of each audio segment to determine a sleep-aiding audio structure file, wherein the sleep-aiding audio structure file is used for indicating the music characteristics of sleep-aiding audio data; and finally performing encoding processing on the sleep-aiding audio structure file to obtain sleep-aiding audio data. Through the method, a user can customize sleep-aiding music with various styles.

Embodiments of the invention provide an audio processing method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining first music data and a processing instruction in a text form associated with the first music data; extracting a first chord proceeding feature and an audio feature of the first music data and a text feature of the processing instruction through a music processing model; processing the audio features through the music processing model according to the first chord proceeding features and the text features to generate second music data; a similarity between a first chord progression feature of the first music data and a second chord progression feature of the second music data is greater than a similarity threshold.

The present invention provides a computer-implemented method for creating a musical educational aid. The method includes selecting a physical musical instrument and accessing a set of dimensions associated with the selected musical instrument. Based on these dimensions, a textless and proportionally accurate representation layout of the selected musical instrument is generated. The method further includes selecting at least one musical scale or chord and placing a subset of musical note labels on the generated layout. These labels correspond to positions for finger placement for each of the notes in the selected musical scale or chord. This invention facilitates the visual learning of musical instruments by providing clear and precise visual representations without the need for textual explanations.

The present disclosure is concerned with A music synthesizer comprising: an input unit having: a note selection input for enabling a user to input a note by selecting the note from a range of notes; a chord type input for enabling the user to input a chord type; and a chord range commencement note input for enabling the user to input a chord range commencement note; and a controller in communication with the input unit, the controller being configured to: generate a plurality of chord notes for a chord, the chord characterized by: the input note as a root note of the chord; the input chord type as a type of the chord; and a chord voicing as a voicing of the chord, the chord voicing comprising one or more chord notes falling within a single octave range commencing with the input chord range commencement note; and output the generated plurality of chord notes to a sound generator.

A musical instrument including a chord player section having at least one chord player operable to play a selected chord, the at least one chord player including a first finger-operable pad having a first center point function and a plurality of first peripheral point functions, the first center point function and the plurality of first peripheral point functions each operable to generate a primary chord having a root note of the chord or a variation of the primary chord having the root note of the chord.

The present invention provides a controllable generation method, system, electronic device and computer-readable storage medium for symbolic music. First, a preset representation module preprocesses the pre-acquired music content and annotation information to form an original data set, and performs masking processing on the original data set according to preset chord condition rules to form an original masked data pair. Then, the original masked data pair is fed into a preset music discrete diffusion framework for repeated training to form a music discrete diffusion model. Subsequently, the music frequency band and music requirements input by the user are preprocessed to form a music content representation and a condition matrix, and the music content representation and the condition matrix are input into the music discrete diffusion model, so that the music discrete diffusion model generates music to output a target music score. In this way, not only can the richness of controllable music generation be achieved, but also the reliability and rationality can be improved.