The present disclosure relates to a system and method for matching performance with score. The method may include acquiring performance information in a preset time period, wherein the performance information is related to a musical device. The method may also include analyzing the performance information and obtaining a played music score in the preset time period, wherein the played music score contains the performance information. The method may further include comparing the played music score with one or more standard music scores. The method may still further include identifying a standard music score from the one or more standard music score based on the comparison of the played music score with the one or more standard music scores, wherein a matching degree between the played music score and the identified standard music score reaches a preset value.

Described herein are techniques for synthesizing waves (e.g., audio waveforms) at various frequencies. For example, techniques described herein may be used to render musical notes by generating audio waveforms. According to some embodiments, the techniques generate a wave table for an audio waveform based on a set of partial waveforms. The system determines a set of notes with a corresponding entry in the wave table and determines a set of partials for each note. The system renders, for each note, an associated waveform comprising the associated number of partials for the note from the set of partial waveforms.

Musical catalog amplification services that leverage or deploy a computer-based musical composition system are described. The computer-based musical composition system employs algorithms and, optionally, artificial intelligence to generate new music based on analyses of existing music. The new music may be wholly distinctive from, or may include musical variations of, the existing music. Rights in the new music generated by the computer-based musical composition system are granted to the rights holder(s) of the existing music. In this way, the musical catalog(s) of the rights holder(s) is/are amplified to include additional music assets. The computer-based musical composition system may be tuned so that the new music sounds more like, or less like, the existing music of the rights holder(s). Revenues generated from the new music are shared between the musical catalog amplification service provider and the rights holder(s).

Musical catalog amplification services that leverage or deploy a computer-based musical composition system are described. The computer-based musical composition system employs algorithms and, optionally, artificial intelligence to generate new music based on analyses of existing music. The new music may be wholly distinctive from, or may include musical variations of, the existing music. Rights in the new music generated by the computer-based musical composition system are granted to the rights holder(s) of the existing music. In this way, the musical catalog(s) of the rights holder(s) is/are amplified to include additional music assets. The computer-based musical composition system may be tuned so that the new music sounds more like, or less like, the existing music of the rights holder(s). Revenues generated from the new music are shared between the musical catalog amplification service provider and the rights holder(s).

A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals. A method and system are also provided for extracting features from digital audio signals and learning, from the extracted features, an artificial neural network model for generating latent-space representations of digital audio signals which take care of selecting salient attributes of the signals that represent psychoacoustic differences between the signals.

A service receives a request from a user of a group of users to perform one or more operations requiring group authentication in order for the operations to be performed. In response, the service provides a first user of the group with a musical seed and an ordering of the group of users. Each user of the group applies a transformation algorithm to the seed to create an authentication claim. The service receives this claim and determines, based at least in part on the ordering of the group of users, an ordered set of transformations, which are used to create a reference audio signal. If the received claim matches the reference audio signal, the service enables performance of the requested one or more operations.

A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals. A method and system are also provided for extracting features from digital audio signals and learning, from the extracted features, an artificial neural network model for generating latent-space representations of digital audio signals which take care of selecting salient attributes of the signals that represent psychoacoustic differences between the signals.

A method and system are provided for extracting features from digital audio signals which exhibit variations in pitch, timbre, decay, reverberation, and other psychoacoustic attributes and learning, from the extracted features, an artificial neural network model for generating contextual latent-space representations of digital audio signals. A method and system are also provided for learning an artificial neural network model for generating consistent latent-space representations of digital audio signals in which the generated latent-space representations are comparable for the purposes of determining psychoacoustic similarity between digital audio signals. A method and system are also provided for extracting features from digital audio signals and learning, from the extracted features, an artificial neural network model for generating latent-space representations of digital audio signals which take care of selecting salient attributes of the signals that represent psychoacoustic differences between the signals.

Various methods for representing audio suitable for use in variational audio encoding are disclosed. A method comprises maintaining, by a computing system, state information for multiple resonator models with different resonant frequencies. The method further comprises iteratively performing a number of different operations, by the computing system for multiple respective samples in a set of audio samples in the time domain. These operations include updating the state information for the multiple resonator models based on the sample amplitude. The operations also include determining respective resonator amplitudes and phases for the updated multiple resonator models and storing, respective resonator amplitude and change-in-phase information for the sample.

Musical catalog amplification services that leverage or deploy a computer-based musical composition system are described. The computer-based musical composition system employs algorithms and, optionally, artificial intelligence to generate new music based on analyses of existing music. The new music may be wholly distinctive from, or may include musical variations of, the existing music. Rights in the new music generated by the computer-based musical composition system are granted to the rights holder(s) of the existing music. In this way, the musical catalog(s) of the rights holder(s) is/are amplified to include additional music assets. The computer-based musical composition system may be tuned so that the new music sounds more like, or less like, the existing music of the rights holder(s). Revenues generated from the new music are shared between the musical catalog amplification service provider and the rights holder(s).