There are provided an electronic musical apparatus, a storage medium storing a recording/reproduction program, and a recording/reproduction method capable of outputting a musical sound phrase and a rhythm phrase after the input musical sound phrase is output. In a case where a recording button is operated, a looper starts to record musical sounds. In this case, the musical sounds (that is, a musical sound phrase) that are being recorded are output from a speaker. Thereafter, in a case where a loop reproduction button is operated, the recording is stopped, and loop reproduction of the recorded musical sound phrase and a rhythm phrase is started. Consequently, it is possible to output musical sounds in which the musical sound phrase and the rhythm phrase are loop-reproduced after the input musical sound phrase is output.

Techniques are provided for generating an output file. One technique involves the steps of generating audio or MIDI content blocks from one or more musical performances; receiving an input file having audio or MIDI music content; generating a harmonic chord map for the input file; using the harmonic chord map to automatically select a subset of the audio or MIDI content blocks, and generating the output file by combining the selected subset of content blocks and the input file. This technique may enable the creation of unique and new musical accompaniments by re-purposing audio or MIDI content from back catalogs and/or out-takes of musical works. The new arrangement may be provided in multiple music styles, genres, or moods and may contain performances from multiple musical instruments, which may be pre-recorded from live instrument performances and/or of MIDI generated musical content.

Example methods, apparatus and articles of manufacture to determine semantic information for audio are disclosed. Example apparatus disclosed herein are to process an audio signal obtained by a media device to determine values of a plurality of features that are characteristic of the audio signal, compare the values of the plurality of features to a first template having corresponding first ranges of the plurality of features to determine a first score, the first template associated with first semantic information, compare the values of the plurality of features to a second template having corresponding second ranges of the plurality of features to determine a second score, the second template associated with second semantic information, and associate the audio signal with at least one of the first semantic information or the second semantic information based on the first score and the second score.

An electronic device comprising circuitry configured to perform (402; 702; 1204) source separation (201) based on a received audio input to obtain a separated source, to perform onset detection (202) on the separated source to obtain an onset detection signal and to mix (405; 706; 1207) the audio signal with the separated source based on the onset detection signal to obtain an enhanced separated source.

A music piece data comparison device includes: a music piece selector that allows a user to select music piece data; a first parameter generator that generates a first parameter from an entirety of the music piece data selected through the music piece selector; a first parameter transmitter that transmits the first parameter generated by the first parameter generator to a server being connected in a manner capable of communication; and a music piece analysis data receiver that receives matched music piece analysis data from the server, in which the server stores the music piece analysis data generated for the music piece data associated with a second parameter generated from the entirety of the music piece data and compares the second parameter with the first parameter to find the music piece analysis data whose second parameter matches the first parameter.

An audio analysis method that is realized by a computer system includes setting a maximum tempo curve representing a temporal change of a maximum tempo value and a minimum tempo curve representing a temporal change of a minimum tempo value in accordance with an instruction from a user, and analyzing an audio signal representing a performance sound of a musical piece, thereby estimating a tempo of the musical piece within a restricted range between a maximum value represented by the maximum tempo curve and a minimum value represented by the minimum tempo curve.

An audio analysis method that is realized by a computer system includes estimating a plurality of beat points of a musical piece by analyzing an audio signal representing a performance sound of the musical piece, receiving an instruction from a user to change a location of at least one beat point of the plurality of beat points, and updating a plurality of locations of the plurality of beat points in response to the instruction from the user.

An audio device that includes a music classifier that determines when music is present in an audio signal is disclosed. The audio device is configured to receive audio, process the received audio, and to output the processed audio to a user. The processing may be adjusted based on the output of the music classifier. The music classifier utilizes a plurality of decision making units, each operating on the received audio independently. The decision making units are simplified to reduce the processing, and therefore the power, necessary for operation. Accordingly each decision making unit may be insufficient to determine music alone but in combination may accurately detect music while consuming power at a rate that is suitable for a mobile device, such as a hearing aid.

A video processing device, which inputs a piece of music and selects and outputs one video of a plurality of video sources, is provided. The video processing device includes a music analyzing part, which analyzes a progress of the piece of music; a video selecting part, which selects the one video from the plurality of video sources; and an output part, which outputs the one video at a timing corresponding to the progress of the piece of music that has been analyzed by the music analyzing part.

A method implemented by a determination engine is provided. The determination engine receives a media dataset comprising target piece music information, target piece audience information, corpus music information, corpus audience information, and corpus preference data. The determination engine determines a subset of the corpus music and preference information and determines at least one surprise factor of the subset of the corpus music and preference information across features at one of a plurality of orders. The determination engine learns a model that estimates a likelihood that time-varying surprise trends across the features achieves a preference level. The determination engine determines at least one surprise factor of the target piece music information across the features at the one of the plurality of orders and predicts, using the model, preference information using the time-varying surprise trends for the target piece music information across the features.