5 results about "Overtone" patented technology

This invention relates to the field of music education technology, specifically to a musical instrument playing training system and method based on real-time pitch monitoring. The system includes a musical instrument acoustic waveform analysis module, a tuning frequency tracking module, a pitch deviation analysis module, a muscle memory reshaping guidance module, and a musical instrument mastery blind spot positioning module. In this invention, by sensing the vibration envelope and reconstructing the energy distribution of the overtone series, the core musical tone components are accurately extracted from a complex acoustic environment, improving the accuracy of fundamental frequency identification and enhancing the ability to capture microscopic pitch drift. This effectively solves the problem of poor anti-interference in traditional detection methods, transforming pitch deviation into a concrete physiological cause analysis, optimizing the depth of performance mechanism analysis and enhancing the physical correction directionality. It achieves precise integration of correction actions and performance rhythm, ensuring the scientific nature of muscle memory reshaping and significantly reducing functional pitch deviation. By tracking the rate of movement deformation and tracing the source of technical bottlenecks, it optimizes musical instrument mastery and improves the level of intelligent music training.

The present application relates to a kind of adaptive band optimization chorus sound separation method and system based on harmonic maintenance, first, the signal is collected by microphone array and time delay compensation and multi-channel time-frequency transformation are carried out;Sound source spatial positioning and initial band division are carried out using MUSIC algorithm;Using the greedy optimization strategy, the band boundary is dynamically adjusted and optimized by maximizing the objective function including signal-to-noise ratio and harmonic integrity measure;Then, the fundamental frequency is obtained by harmonic analysis and detection, and the time-varying harmonic trajectory is tracked across frames by Viterbi algorithm;On this basis, combined with MVDR beam forming and the introduction of harmonic maintenance factor, enhanced time-frequency representation is obtained by soft time-frequency mask;Finally, inverse short-time Fourier transform time-domain reconstruction and synchronous harmonic phase are carried out.The present application realizes the accurate allocation of voice part spectrum, effectively reduces the overlap interference, protects the overtone column of human voice while accurately separating, significantly improves the timbre fidelity and naturalness of separated voice part.

This invention discloses a real-time piano timbre simulation method and system based on machine learning, relating to the field of audio signal processing technology. The method includes: data acquisition and multi-dimensional annotation, acquiring multiple types of piano audio, covering techniques and seven dynamic levels, and simultaneously acquiring information such as key presses and techniques; audio preprocessing, including pre-emphasis compensation for high frequencies, Hanning window framing, Fourier transform to frequency domain, spectral subtraction for noise reduction and normalization; multi-dimensional feature extraction, extracting static features such as MFCC and spectral parameters, dynamic features such as first- and second-order differences, and overtone structures; two-stage model training, using stacked autoencoders for dimensionality reduction; real-time parsing, filtering and converting acquired performance data into parameter sequences; timbre synthesis, where the model generates a spectrum and performs an inverse Fourier transform into a waveform; and dynamic optimization, receiving user feedback. This invention solves the problems of traditional simulation methods; the two-stage model enhances timbre coherence, dynamic control achieves low latency, and multi-scenario adaptation and feedback optimization meet specific needs.

The invention discloses a piano tone real-time simulation method and system based on machine learning, and relates to the technical field of audio signal processing, and the method comprises the steps: data collection and multi-dimensional marking, collection of multiple types of piano audios, skill coverage and seven levels of strength, and synchronous collection of information such as keys and skill; performing audio preprocessing, pre-emphasis high frequency compensation, Hanning window framing, Fourier transform to frequency domain, spectral subtraction denoising and normalization; multi-dimensional feature extraction: extracting static features such as MFCC and spectrum parameters, dynamic features such as first-order and second-order differences and an overtone structure; carrying out two-stage model training, and carrying out stacking auto-encoder dimensionality reduction; analyzing and acquiring playing data in real time, filtering and converting into a parameter sequence; synthesizing timbre, generating a frequency spectrum by a model, and performing inverse Fourier transform to form a waveform; and dynamically optimizing and receiving user scores. According to the invention, the traditional simulation problem is solved; the dual-stage model enhances tone coherence, and dynamic control realizes low delay, multi-scene adaptation and feedback optimization fitting requirements.

The invention relates to the technical field of music data processing, in particular to a violin playing auxiliary system based on artificial intelligence, and the system comprises an audio collection module which employs a microphone array to capture a violin playing sound field, and generates an original audio stream. A coding adaptation module generates a transmission code stream while extracting side information metadata including a noise masking threshold and a transient signal flag. The feature extraction module outputs a high-dimensional feature vector. The reconstruction module generates reconstructed audio. And the evaluation feedback module performs quantitative evaluation on the reconstructed audio, generates scores of intonation deviation degree, string rubbing depth and overtone purity, feeds the scores back to the coding adaptation module, and dynamically adjusts frequency weighting parameters of the psychological acoustic model. The music interface module outputs reconstructed audio and structured evaluation data. The system drives the coding adaptation module to adaptively optimize a compression strategy through score feedback of the evaluation feedback module, and the reconstruction module optimizes a generation process by using historical evaluation data to form a closed-loop data stream.