System, apparatus and method for determining semantic information from audio, where incoming audio is sampled and processed to extract audio features, including temporal, spectral, harmonic and rhythmic features. The extracted audio features are compared to stored audio templates that include ranges and/or values for certain features and are tagged for specific ranges and/or values. Extracted audio features that are most similar to one or more templates from the comparison are identified according to the tagged information. The tags are used to determine the semantic audio data that includes genre, instrumentation, style, acoustical dynamics, and emotive descriptor for the audio signal.

A system for providing a user a virtual exercise in playing a music instrument relative to the user's skill characteristics includes: a processing entity and a memory entity for processing and storing data, respectively to execute the system functions, and a data transfer entity for receiving and transmitting data, the system configured to: obtain musical notation data, analyze it to assign the musical piece to which such data pertains a number of difficulty characteristics with scalar values, provide the user with a number of musical pieces, with known difficulty characteristics, as virtual exercises to be completed by playing an instrument, obtain user performance data of completed virtual exercises, analyze the user performance data to determine and assign the user with a number of skill characteristics values in accordance with the difficulty characteristic values of the completed musical pieces, and determine a musical piece for the user as a virtual exercise.

The intelligent accompaniment generating system includes an input module, an analysis module, a generation module and a musical equipment. The input module is configured to receive a musical pattern signal derived from a raw signal. The analysis module is configured to analyze the musical pattern signal to extract a set of audio features, wherein the input module is configured to transmit the musical pattern signal to the analysis module. The generation module is configured to obtain a playing assistance information having an accompaniment pattern from the analysis module, wherein the accompaniment pattern has at least two parts having different onsets therebetween, and each onsets of the at least two parts is generated by an algorithm according to the set of audio features. The musical equipment includes a digital amplifier configured to output an accompaniment signal according to the accompaniment pattern.

A method of producing a light animation with a rhythm of music is disclosed. An electronic device performs Fourier series transform on a sound signal of music produced from at least one musical instrument, so as to obtain a rhythm diagram of the sound signal. The operation to extract a rhythm change point of the rhythm diagram is performed, and when the intensity of the rhythm diagram has a change from increase to decrease, the time point of the change is used as the rhythm change point and the electronic device transmits a lighting control signal to a light emitting device. After receiving the lighting control signal, the light emitting device emits light based on the lighting control signal, and the light emitted from the light emitting device continues to form the light animation, thereby improving overall performance appreciation of the music for audiences.

A chord identification method selects from among a plurality of chord identifiers a chord identifier that corresponds to an attribute of a piece of music represented by an audio signal, where the plurality of chord identifiers corresponds to respective ones of a plurality of attributes relating to pieces of music; and identifies a chord for the audio signal by applying a feature amount of the audio signal to the selected chord identifier.

A computer receives a first audio content item and applies a process to generate a representation of first audio content item. A portion is extracted from the representation of the first audio content item. A first representative vector that corresponds to the first audio content item is determined by applying a variational autoencoder (VAE) to a first segment of the extracted portion the audio content item. The computer stores the first representative vector that corresponds to the first audio content item.

A music beat point detection method includes: performing a frame processing on a music signal to obtain a frame signal; obtaining a power spectrum of the frame signal; performing sub-band decomposition on the power spectrum, and decomposing the power spectrum into at least two sub-bands; performing a time-frequency domain joint filtering on a signal of each sub-band according to a beat type corresponding to each sub-band; obtaining a to-be-confirmed beat point from the frame signal of the music signal according to a result of the time-frequency domain joint filtering; and obtaining a beat point of the music signal according to a power value of the to-be-confirmed beat point.

According to an embodiment, there is provided a system and method for automatic AI controlled loop based song construction. It provides and benefits from a machine learning AI in a audio loop selection engine for the generation of a song structure and for the selection of fitting audio loops from a database of audio loops. In one embodiment, the instant method provides a music generation process that utilizes an AI system that has been trained and validated on a music item database to complete the creation of a music item given an incomplete song that was started but not finished by a user.

Systems and methods for versioning audio elements used in generation of music are provided. An example method includes receiving musical format data associated with a plurality of audio elements of a melody; determining, based on the musical format data, harmonic and melodic characteristics of each of the plurality of audio elements; matching the harmonic and melodic characteristics to a plurality of chord progressions using predetermined music theory rules, counterpoint rules, and rhythm matching rules; deriving, based on the matching and predetermined melodic movement rules, from the plurality of chord progressions, melodic movement characteristics applicable to using in versioning; and creating, based on the predetermined music theory rules and the melodic movement characteristics, versions of the audio elements that match the chord progressions.

The invention provides for the evaluation of semantic closeness of a source data file relative to candidate data files. The system includes an artificial neural network and processing intelligence that derives a property vector from extractable measurable properties of a data file. The property vector is mapped to related semantic properties for that same data file and such that, during ANN training, pairwise similarity/dissimilarity in property is mapped, during towards corresponding pairwise semantic similarity/dissimilarity in semantic space to preserve semantic relationships. Based on comparisons between generated property vectors in continuous multi-dimensional property space, the system and method assess, rank, and then recommend and/or filter semantically close or semantically disparate candidate files from a query from a user that includes the data file. Applications apply to search and compilation tools and particularly to recommendation tools that provide a succession of logical progressive associations that link between disparate file content in source and destination files.