4 results about "Harmonic structure" patented technology

This invention belongs to the field of music data processing and relates to a symbolic music classification method and system based on Tonnetz spatiotemporal graphs. The method generates a two-dimensional grid of symbolic music data, consisting of time frames and pitches, and defines graph nodes and initial features for each grid point. Discrete note events are organized into structured nodes. Based on this two-dimensional grid, graph nodes corresponding to pitches that satisfy Tonnetz interval relationships are connected within the same time frame to form spatial edges. These spatial edges explicitly introduce consonant interval relationships between pitches, allowing the model to directly obtain structural information with musicological significance without relying on large amounts of data for self-learning, thus solving the problem of insufficient utilization of harmonic structure. Furthermore, graph nodes with the same pitch or pitch differences within a preset range are connected between adjacent time frames to form temporal edges. These temporal edges directly encode the continuous and stepwise motion of unison parts during voice progression, enabling the model to capture the dynamic evolution of music in the time dimension and solving the problem of missing voice progression characterization.

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.

This invention discloses a method for recognizing vocal styles based on time-frequency refined analysis. The process is as follows: First, a fundamental frequency estimation and harmonic labeling step is performed. The short-duration vowel portion of the vocal signal is first used for fundamental frequency estimation. This estimation employs a frequency estimation algorithm combining time-domain autocorrelation and narrowband spectral energy. Then, based on the estimated fundamental frequency, adaptive harmonic labeling is performed on the spectrum to accurately identify all harmonics. Next, a time-frequency refined analysis step is performed. The vocal signal is refined and features are extracted from both the time and frequency domains, with a focus on periodic variations and harmonic structures. Finally, a support vector machine (SVM) model training and recognition step is performed. The extracted features and corresponding vocal style labels are used to train the SVM model. After training, the model can be used for vocal style recognition. The style is obtained by using the feature vectors extracted from the vocal signal as input.

The application provides a speech synthesis method and device, computer equipment and a storage medium. The method comprises obtaining a target score to be processed, and extracting score features in the target score; determining acoustic features corresponding to the score features according to the score features and a pre-trained acoustic model; generating an initial waveform with a harmonic structure based on fundamental frequency information in the acoustic features; and generating a target audio corresponding to the target score according to the acoustic features and the initial waveform. After the acoustic model is used to obtain acoustic features including a fundamental frequency, energy, a mel-frequency spectrum and the like, an initial waveform with a harmonic structure is generated based on the fundamental frequency information in the acoustic features, which can effectively avoid possible fundamental frequency jitter in the subsequently synthesized audio, guarantee the pitch continuity and pronunciation stability of the target audio generated based on the acoustic features and the initial waveform, and thus improve the speech synthesis effect.