A device for automatically generating a real time accompaniment of input music data includes a music input that receives music data. A music analyzer analyzes received music data to obtain a music data description including one or more characteristics of the analyzed music data. A query generator generates a query to a music database including music patterns and associated metadata including one or more characteristics of the music patterns, the query being generated from the music data description and from an accompaniment description describing preferences of the real time accompaniment and/or music rules describing general rules of music. A query interface queries the music database using a generated query and receives a music pattern selected from the music database by use of the query. A music output outputs the received music pattern.

A recommendation of at least one of multiple audio samples or sets of audio samples to combine with a particular audio sample or set of audio samples is automatically generated. The recommendation is generated by determining the rhythmic compatibility as well as the harmonic compatibility of the particular audio sample or set of samples with each of the multiple audio samples or sets of audio samples. For each of the multiple audio samples or sets of audio samples, a compatibility rating is generated based on the rhythmic compatibility and the harmonic compatibility of the audio sample or set of audio samples with the particular audio sample or set of audio samples. At least one of the multiple audio samples or sets of audio samples is presented by a computing device as a recommendation to combine with the particular audio sample or set of audio samples.

In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products. A computing device stores reference song characterization data and receives digital audio data. The computing device determines whether the digital audio data represents music and then performs a different process to recognize that the digital audio data represents a particular reference song. The computing device then outputs an indication of the particular reference song.

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.

An artificial intelligence (AI) method includes generating a first musical interaction behavioral model. The first musical interaction behavioral model causes an interactive electronic device to perform a first set of musical operations and a first set of motional operations. The AI method further includes receiving user inputs received in response to the performance of the first set of musical operations and the first set of motional operations and determining a user learning progression level based on the user inputs. In response to determining that the user learning progression level is above a threshold, the AI method includes generating a second musical interaction behavioral model. The second musical interaction behavioral model causes the interactive electronic device to perform a second set of musical operations and a second set of motional operations. The AI method further includes performing the second set of musical operations and the second set of motional operations.

Audio fingerprinting includes obtaining audio samples of a piece of audio, generating frequency representations of the audio samples, identifying increasing and decreasing energy regions in frequency bands of the frequency representations, and generating hashes of features of the piece of audio. Each hash of features corresponds to portions of the identified energy regions appearing in a respective time window. Each feature is defined as a numeric value that encodes information representing: a frequency band of an energy region appearing in the respective time window, whether the energy region appearing in the respective time window is an increasing energy region or whether the energy region appearing in the respective time window is a decreasing energy region, and a placement of the energy region appearing in the respective time window.

The disclosed embodiments provide a system that performs a sound-recognition operation. During operation, the system recognizes a sequence of sound primitives in an audio stream, wherein a sound primitive is associated with a semantic label comprising one or more words that describe a sound characterized by the sound primitive. Next, the system feeds the sequence of sound primitives into a finite-state automaton that recognizes events associated with sequences of sound primitives. Finally, the system feeds the recognized events into an output system that generates an output associated with the recognized events to be displayed to a user.

Provided a haptic feedback method, including: step S1 of algorithmically training an audio clip containing a known audio event type to obtain an algorithm model; and step S2 of obtaining an audio, identifying the audio by the algorithm model to obtain different audio event types in this audio, matching, according to a preset rule, the audio event types with different vibration effects as a haptic feedback and outputting the haptic feedback. Compared with the related art, the present haptic feedback method provides users with real-time haptic feedback when applied to a mobile electronic product, thereby achieving excellent use experience of the mobile electronic product.

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.

In at least one embodiment, a method of performing automatic transcription of musical content included in an audio signal received by a computing device is provided. The method includes processing, using the computing device, the received audio signal to extract musical information characterizing at least a portion of the musical content and generating, using the computing device, a plurality of musical notations representing alternative musical interpretations of the extracted musical information. The method further includes applying a selected one of the plurality of musical notations for transcribing the musical content of the received audio signal.