71 results about "Sequence (music)" patented technology

A system, computer readable storage medium, and method for communicating musical data over a network, comprising: connecting one or more player modules to a network; providing an unassigned channel of prerecorded music data; providing an assigned channel of prerecorded music data; displaying the assigned channel; playing the unassigned channel while not playing the assigned channel; converting musical performance to music performance data; evaluating the music performance data in regard to the assigned channel; and simultaneously delivering a representation of the evaluation over the network. There may also be a graphical user interface, including a virtual instrument positioned substantially at a top portion of the graphical interface. Additionally, included are pluralities of musical notes in sequence, having a rhythmic pattern, each note being represented by a game object, wherein the game objects are directed upward, in substantially straight trajectories, toward the virtual keys on the virtual musical instrument corresponding to the musical notes.

A method and apparatus for providing musical training is disclosed. The method / apparatus allows a user to practice voice lessons, ear training lessons, songs, and / or various musical instruments that rely on good pitch perception (such as a violin or a brass instrument). The method / apparatus displays a sequence of music notes to the user via a combination of conventional music staffs (such as a grand staff) and instances of a pitch-based representation. The method / apparatus also displays the accuracy of the user in hitting notes, the quality of his / her tone if supported, changes to his / her vocal range if desired, and tracks the user's practice history. The method / apparatus further provides features such as automatic vocal range detection, adjustment of lessons to a user's vocal range, and a display method that makes changes to the pitches of music notes clear during musical key / scale changes.

The invention provides a system for building a music classification model, a system for recommending music and a corresponding method, which are used for solving the problems of non scientific music classification module and poor music recommending effect in the prior art. The system for building the music classification model comprises a first characteristic extraction unit, a first characteristic split joint unit and a model training unit. The system for recommending music comprises a second characteristic extraction unit, a second characteristic split joint unit, a matching unit and a recommending unit. The method for building the music classification module comprises the following steps that different dimensions of acoustic characteristic vectors are extracted by aiming at each piece of music in training data; different dimensions of acoustic characteristic vectors are split into super vectors according to the preset sequence by aiming at each piece of music; and the super vectors of each piece of music in different music styles are utilized for training a GMM (Gaussian mixture model). The GMM utilized by the method for recommending music is obtained by the method for building the music classification model.

The present identifies collections of digital music and sound that effectively elicit particular emotional responses as a function of analytical features from the audio signal and information concerning the background and preferences of the subject. The invention can change emotional classifications along with variations in the audio signal over time. Interacting with a listener, the invention locates music with desired emotional characteristics from a central repository, assembles these into an effective and engaging “playlist” (sequence of songs), and plays the music files in the calculated order to the listener.

A system, computer readable storage medium, and method for communicating musical data over a network, comprising: connecting one or more player modules to a network; providing an unassigned channel of prerecorded music data; providing an assigned channel of prerecorded music data; displaying the assigned channel; playing the unassigned channel while not playing the assigned channel; converting musical performance to music performance data; evaluating the music performance data in regard to the assigned channel; and simultaneously delivering a representation of the evaluation over the network. There may also be a graphical user interface, including a virtual instrument positioned substantially at a top portion of the graphical interface. Additionally, included are pluralities of musical notes in sequence, having a rhythmic pattern, each note being represented by a game object, wherein the game objects are directed upward, in substantially straight trajectories, toward the virtual keys on the virtual musical instrument corresponding to the musical notes.

A method and apparatus for composing and teaching of music while learning to read music is provided that involves a user following a supported sequence of tasks that guide creativity of the user to construct a musical composition. The musical composition is constructed by assigning alphabetical characters to colors and musical notes to form a chart, forming words and phrases using the alphabetical characters, and cross-referencing the words and phrases to the chart. Various interactive components and computer software facilitate use of the method and process.

An acoustic transformation system and method. A specific embodiment is the transformation of acoustic speech signals produced by speakers with speech disabilities in order to make those utterances more intelligible to typical listeners. These modifications include the correction of tempo or rhythm, the adjustment of formant frequencies in sonorants, the removal or adjustment of aberrant voicing, the deletion of phoneme insertion errors, and the replacement of erroneously dropped phonemes. These methods may also be applied to general correction of musical or acoustic sequences.

The invention discloses an interactive teaching method and system. The interactive teaching method comprises the following steps: acquiring a played audio signal; matching the note sequence of the played audio signal with the note sequence of the standard music score, and displaying a matching result; if the matching result is correct, the user continuing playing; otherwise, executing a music score singing process. The music score singing process comprises the steps that audio signals of music score singing of the user are collected; the singing music score content of the singing music score audio signal is matched with the note sequence of the standard music score, and the matching result is displayed; and if the matching result is correct, the user plays. According to the interactive teaching method and system, music score content recognition and correction before musical instrument playing and correction of wrong content after playing are performed by using the mode in the musical instrument learning process of the user so that the musical instrument learning efficiency of the user can be enhanced.

The invention provides a method, a device and a system for controlling a lighting effect and a storage medium, and relates to the technical field of light control. The method comprise the following steps of: acquiring a music sampling signal, framing the music sampling signal, and determining a data signal of each frame; determining a corresponding music rhythm according to the average energy of the data signal; and according to the music rhythm, controlling the lighting effect of a lighting element. According to the invention, the music rhythm is effectively determined by utilizing the average energy in the music sampling signal, and the lighting element is controlled to change in sequence along with the music rhythm, so that various lamplight effects responding along with the music rhythm are realized.

The invention discloses an automatic composition system and method for note vectors based on contextual information, and the method comprises the following steps: firstly carrying out the preprocessing of an MIDI music file through an MIDI music preprocessing module, and extracting a main melody note sequence comprising two phonemes: pitch and pitch; sending the main melody note sequence to a notevector coding module, wherein the module is used for training and generating a note vector with contextual information, the note vector with the contextual information enables subsequent compositionnetwork note prediction accuracy to be improved, and meanwhile the generated note vector is used for representing a main melody note sequence again; then, using the main melody note sequence which isgenerated by the note vector encoding module and represented by note vectors as training data to be sent to the automatic composition network module, wherein the module is used for generating a note sequence to be composed; and finally, restoring the note sequence to be composed into an MIDI format through an MIDI music synthesis and playing module, and playing the note sequence.

The invention discloses a real-time dance movement generation method and system based on music rhythm. The method comprises the following steps: segmenting a dance movement sequence and a music sequence into dance movement fragments and music fragments; extracting motion features of the dance motion segments and music features of the music segments; carrying out rhythm feature matching on the dance movement segment and the music segment to obtain a dance movement segment matched with the rhythm of the music segment; analyzing the connectivity among the dance movement segments matched with therhythm of the music segment; performing intensity feature matching on each dance movement segment and the music segment which are determined to be connectable, and finally determining at least two dance movement segments matched with the music segment according to the intensity matching degree; and combining the finally determined dancing action segments into a dancing action sequence with naturaltransition. According to the invention, automatic arrangement of dancing is realized.

The invention provides a music beat detection method and system, and the method comprises the steps: carrying out the left and right channel addition of a dual-channel audio after music is received, obtaining a music PCM type data sequence, carrying out the grouping addition of the data sequence, solving an average value for simplification, and obtaining a simplified audio signal; carrying out autocorrelation function calculation on the simplified audio signal, and then carrying out wave crest detection to obtain all wave crests; carrying out envelope extraction on the simplified audio signalto obtain an audio envelope signal, carrying out first-order difference processing, half-wave rectification processing and weighted compensation processing on the audio envelope signal to obtain an audio peak signal, and detecting and outputting all beat points by utilizing a dynamic threshold wave crest; and utilizing all the wave crests and all the beat points to obtain an optimal beat path through multi-path search, and recording an audio rhythm and beat points corresponding to the optimal beat path. The problems of music rhythm detection and specific rhythm point accurate positioning are solved, and the rhythm value is estimated by searching for a possible rhythm value and an autocorrelation function convenient to implement.

The invention relates to the technical field of intelligent music generation, in particular to an emotional music generation method based on a deep neural network and music element driving. The method comprises the following steps: acquiring an emotion type specified by a user, and converting the emotion type into a corresponding music element feature; inputting the music element features into a pre-established and trained emotional music generation model to obtain a corresponding emotional music sequence; and decoding and synthesizing the emotion music sequence to obtain emotion music. According to the invention, music is generated by using an artificial intelligence algorithm, and emotion factors are fused into the intelligent music generation system, so that the artistic appeal and emotion rendering ability of the intelligent music are improved; and emotional music generation does not depend on a large amount of manually-marked music data.

The invention provides a superposition enhancement neural modulation method and a device based on music and an energy wave function. The method comprises the following steps: generating a music stimulation sequence; analyzing the music stimulation sequence to obtain a stimulation signal sequence corresponding to stimulation intensity and stimulation time; and performing stimulation output by a stimulation controller according to the stimulation signal sequence. According to the invention, the stimulation timestamp and the intensity of the stimulation controller are recoded by using the inherent rhythm and the energy wave function of the music to induce the superposition enhanced neural modulation effect, so that the method can be applied to various treatment and evaluation scenes, for example, post-stroke limb dyskinesia, cognitive impairment, aphasia, senile dementia, Parkinson's disease, spinal cord neural network excitability, disturbance of consciousness and the like.

The invention discloses a music main melody track identification method based on a neural network. The method comprises the following steps of extracting a plurality of features by utilizing an MIDI text to measure and describe each track in music, and automatically identifying and judging the main melody track of the music according to the basic features of the music by constructing a neural network model; the method comprises the following steps of (1) extracting the information of notes in music and calculating characteristic values of all audio tracks; (2) converting an audio track characteristic value into a two-dimensional neural network input matrix; (3) training a main melody audio track identification model based on a neural network; and (4) performing music main melody track identification by using the trained neural network. According to the method, important music features are extracted by using the MIDI file, and automatic identification is carried out after digital processing by using the neural network, so the main melody audio track can be accurately and efficiently detected and identified from the MIDI file; the music main melody track identification method based on the neural network is provided for research on automatic generation of music sequences.

The invention relates to the technical field of music generation and specifically relates to a music melody generation method based on pitch contour curve. The method comprises the following steps that 1, long-term structure information of the pitch profile curve is extracted in a frequency domain, the long-term structure information comprises a low-frequency part in a frequency domain sequence ofthe pitch contour curve, and a long-term trend rule of melody is reflected; 2, through fitting the long-term structure information by using a neural network with label control, long-term structure information corresponding to the label is generated; and 3, another neural network is trained by using the long-term structure information and the melody length information of the music data, so that the music data has the capability of speculating the melody length information according to the long-term structure information. According to the invention, the music melody with a controllable long-term structure is generated by using the frequency domain characteristic of the pitch contour curve, so that the music generated by the method is closer to the real music distribution than the music generated by a long-short-term network.

The invention relates to a repeated-melody-based music segmentation boundary identification method, and belongs to the technical field of audio signal processing. The method comprises the following steps: 1) extracting chroma features of an audio, performing zero filling at the head and the tail, aggregating every adjacent N frames to form a new frame vector, and forming a new frame feature vectorsequence by all frame vectors; 2) calculating the Euclidean distance between each frame vector in the frame feature sequence and other frame vectors to obtain a self-similarity matrix S; 3) based onthe self-similarity matrix S, obtaining a set Ni of nearest neighbor frames of the ith frame vector, and obtaining a recurrence plot R of the self-similarity matrix S; 4) performing time delay processing on the recurrence plot R to obtain a time delay matrix L; 5) performing line segment normalization and denoising on the L, and performing reverse time delay processing to obtain a recurrence plotR', and 6) detecting all line segments and performing line segment clustering, and performing processing in sequence from the cluster with the most line segments to obtain a music segmentation boundary point set B. The identification capability for repeated melodies in music can be improved, and the music can be segmented in a shorter time.

The invention discloses an MIDI rhythm type identification method. The method comprises the steps: splitting an MIDI fragment into a bass area and a middle-high pitch area according to voice thresholddistribution; dividing the MIDI fragment into N units according to a certain unit length, dividing the time length corresponding to each unit into a plurality of time steps, and respectively countingthe occurrence frequency of notes of each time step in a bass region and a middle-high pitch region; aiming at the bass zone and the middle-high pitch zone, respectively counting the average frequency of notes appearing on the time steps corresponding to the N units, wherein a sequence formed by the average frequency according to time steps represents rhythmic characteristics. The invention alsodiscloses a rhythm type-based music style determination method and a rhythm type-based playing mode determination method. The three methods are simple, and a new thought is provided for automatic composition and automatic accompaniment.

The embodiment of the invention discloses a melody similarity evaluation method and device, terminal equipment and a storage medium, and relates to a data analysis and similarity measurement technology, and the method comprises the steps: obtaining two pieces of to-be-analyzed music, and recognizing decorative sounds of the two pieces of music; simplifying the decorative tones of the two pieces ofmusic, and performing feature extraction on the two pieces of music to respectively obtain melody feature sequences of the two pieces of music; respectively carrying out melody segmentation on the melody characteristic sequences of the two pieces of music to obtain a plurality of melody sub-sequences with different lengths corresponding to the melody characteristic sequences of the two pieces ofmusic; and generating a similarity feature map of the two pieces of music according to the melody sub-sequences of the two pieces of music.

The embodiment of the invention discloses an accompaniment method for actively following music signals and related equipment. The accompaniment method comprises the following steps that: first audio data of a performer in the performance process of a target track are acquired; an observation sequence is determined according to the first audio data and music scores corresponding to the target track, wherein the observation sequence is a corresponding sequence of each note in the first audio data and a performance moment of each note; a prediction sequence is determined according to the observation sequence and the music scores, wherein the prediction sequence is a corresponding sequence of each note of the first audio data and the predicted performance moments; and according to the observation sequence and the prediction sequence, the performance moment of the musical note of the next performance of the performer is predicted, and according to the corresponding relationship between the music scores and accompaniment audios, accompaniment audio data corresponding to the musical note of the next performance is controlled to be played at the predicted performance moment. According to the method, the occurrence time of the next note to be performed of the performer can be accurately predicted, the accompaniment is automatically matched with the performance so as to be played, and the interaction effect between the accompaniment and the performance is improved.

The invention relates to the technical field of music auxiliary teaching, and discloses a rhythm vision singing visualization method, a system and a device, and the method comprises the following steps: S1, inputting beat numbers and a plurality of notes in sequence, and presetting a beat time value and reference loudness; S2, sequentially obtaining a beat number and a plurality of notes according to the sequence; S3, determining the beatstrength according to the beat number, determining the rhythm strength according to the note sequence, and synthesizing or calling a corresponding audio according to the beat strength, the rhythm strength and the reference loudness; S4, determining information such as the position of an indication module according to the beat number and the note information; and S5, controlling the indication module to move according to a trajectory, and controlling a sound production module to synchronize the audio sound production. The beneficial effects of the invention are that a music note unit module and a beat number unit module which can be freely combined are arranged, and the acoustic model synthesis technology is combined, so that the rhythm content of autonomous sighting and singing practice is enriched, and the individual demands of users are met; and by arranging a visualization module, the difficulty of video singing rhythm practice is reduced.

The invention discloses a Chinese national pentatonic emotion recognition method and system. Firstly, by decoding MIDI files, extracting all data representing pitches, removing repeated tones, and sorting the pitches to obtain a pitch sequence; The IACTS algorithm identifies this set of data and checks the results to accurately judge its mode; after finding the palace tone, it will classify all the notes in the music according to the mode interval relationship based on the palace tone, if all the notes can be Accurately classify according to the relationship with the palace tone interval, then the verification is passed, and the mode is finally determined. On the contrary, if there is a special interval outside the mode, the verification will not pass; finally, according to the currently recognized division method: the color of the feathers and the color of the feathers, the emotional color of Chinese folk music is judged.

The invention provides a harmonics learning method and device and electronic equipment. The method comprises the steps of receiving music score sequence data, and converting the music score sequence data into four-part harmony chord progression data; based on the four-part harmony chord progression data, carrying out the following judgments: chord integrity judgment, chord in-situ judgment, chordrepeated sound judgment, chord function name judgment, open dense arrangement judgment, sound part intersection judgment, chord progression function judgment, chord progression sound part intersectionjudgment and chord progression direction judgment; and based on a judgment result, determining corresponding note data in the music score sequence data through the corresponding relationship, and adding the judgment result to the note data, so that the music score display module displays corresponding errors while displaying notes. According to the invention, the writing result of a student can be analyzed and judged in remote teaching, and error correction can be selectively carried out according to the instruction, thereby achieving the purpose of enhancing the music feeling and learning effect of the student.

A system and method for making musical decisions is provided. The approach is based on algorithms which compose music driven by an input of desired emotional tension over time. A method for abstraction and quantification of musical structures is detailed, as well as the application of this method in a generative algorithm that produces musical sequences. The overall goal of this method is to use a set of abstractions and guidelines to generate emotionally appropriate new music in real time.

The invention discloses a music score generating method and a music score generating device, belonging to the technical field of terminals. The music score generating method comprises the following steps: acquiring audio signals of a target multi-media file; according to the frequency of the audio signal of each note moment, determining the pitch of the audio signal of each note moment; forming a pitch sequence by adopting the pitches of the audio signals of the note moments, and determining a note sequence of the target multi-media file according to the preset intervals among the pitch sequence and the note moments; dividing the note sequence into a plurality of note subsequences, and acquiring the rhythm of each note subsequence; and forming the music score of the target multi-media file by adopting the note subsequences and the rhythms of the note subsequences. According to the music score generating method and the music score generating device, through the frequencies of the audio signals of the note moments, the pitches, note sequence and the rhythms are obtained, the music score of the target multi-media file is determined, and thus the accuracy in generating the music score is improved.

The invention discloses a music recommendation method and device fusing user interests, equipment and a storage medium. The method comprises the steps that a sequence length vector is calculated according to the actual length and the maximum length of a user music playing sequence; determining a portrait feature vector of the user, a short-term interest feature vector of the user and a long-term interest feature vector of the user in the music scene; determining weight vectors corresponding to the portrait feature vector of the user, the short-term interest feature vector of the user and the long-term interest feature vector of the user; based on the weight vector and the sequence length vector, performing model dynamic fusion on an initial music recall model to obtain a target music recall model; and recommending music to the user based on the target music recall model. Model dynamic fusion is performed according to the sequence length vector and the weight vector, and music library recommendation is performed based on the fused model, so that a user feels that the music library of music is rich and colorful, and final user loss is avoided.

The invention belongs to the field of modern music industry, and particularly relates to a melody generation method of a three-part profit-and-loss method, which specifically comprises the following steps of: inputting an initial melody frequency, a melody number and a decimal digit through an input module, calculating a corresponding numerator value, a denominator value and a decimal melody value by utilizing a calculation module, and outputting through an output module, so that melody can be conveniently combined with back-end music application software, and the aim of applying a multi-melody system to the music software can be achieved. According to the invention, the output melody sequence can be further played by various musical instrument software, so that the melody sequence can be conveniently used by music employees.

The present disclosure provides a music synthesis method, device, electronic equipment, and storage medium. The method includes: acquiring the current hidden variable; inputting the current hidden variable into each cyclic neural network unit included in the cyclic neural network model to obtain each The music features output by the cyclic neural network unit; through the generator trained in the anti-neural network model, the music features output by each cyclic neural network unit are converted into corresponding music sequences; wherein, the cyclic neural network unit and the generator are in The discriminator in the confrontational neural network model is obtained after the training is completed; the music sequence is sequentially spliced ​​to obtain the synthesized music sequence corresponding to the current hidden variable. Each music sequence depends on the current hidden variable and the music feature output by at least one cycle unit before each cycle unit, so that the length of the sequence on which each music sequence depends is shorter, and artificial intelligence can synthesize longer music.