A tactile music learning apparatus converts sound data of a user's voice corresponding to original music into first tactile data including tactile information, generates a synchronized tactile pattern by synchronizing the first tactile data with second tactile data including tactile information corresponding to sound data of the original music, and transfers the synchronized tactile pattern to a tactile reproducing apparatus to allow the tactile reproducing apparatus to reproduce the synchronized tactile pattern.

An input musical sound signal is input to band pass filters, and pass musical sound signals for each sound pitch are acquired. Total level ratios based on a total of levels of a sound pitch lower than those of the pass musical sound signals are calculated from the levels that are levels of the pass musical sound signals, and output coefficients are acquired on the basis of the total level ratios. Levels of octave musical sound signals acquired by converting the pass musical sound signals into sound pitches lower than those of the pass musical sound signals by one octave are multiplied by the output coefficients. In accordance with this, the octave musical sound signals of a low sound pitch can be extracted from among the octave musical sound signals.

An electronic musical instrument includes a plurality of performance elements that specify pitch data; a sound source that produces musical sounds; and a processor configured to perform the following: when a user performance of the plurality of performance elements satisfies a prescribed condition, instructing the sound source to produce automatic arpeggio playing sounds corresponding to pitch data specified by the user performance; and when the user performance of the plurality of performance elements does not satisfy the prescribed condition, instructing the sound source to produce a sound of a pitch data specified by the user performance without producing the automatic arpeggio playing sound.

An electronic musical instrument includes: a plurality of performance elements that specify pitch data; a sound source that produces musical sounds; and a processor configured to perform the following: when a user performance of the plurality of performance elements satisfies a prescribed condition, instructing the sound source to produce a sound of a first timbre and a sound of a second timbre, both corresponding to a pitch data specified by the user performance; and when the user performance of the plurality of performance elements does not satisfy the prescribed condition, instructing the sound source to produce the sound of the first timbre corresponding to the pitch data specified by the user performance and not instructing the sound source to produce the sound of the second timbre.

A driving sound library provides driving sounds classified in various themes to a user by analyzing frequency characteristics and temporal characteristics of sound sources classified into categories and determining a chord corresponding to each sound source based on the frequency characteristics and the time characteristics. Modulated sound sources in which each sound source is modulated are generated by applying a chord corresponding to each sound source. The driving sound sources are generated using the sound sources and the modulated sound sources as input data. Pitches of the driving sound sources are then changed based on the driving sound sources and engine sound orders corresponding to an engine RPM of a preset range. Scores for each theme for the driving sounds are generated.

A non-transitory computer-readable storage medium stored with an automatic music arrangement program, and an automatic music arrangement device are provided. An outer voice note having a highest pitch among notes of which sound production start times are approximately the same in a melody part acquired from musical piece data is identified. A melody part acquired by deleting inner voice notes of which sound production starts within a sound production period of the outer voice note and of which pitches are low from the melody part is generated. Candidate accompaniment parts in which root notes of chords of chord data of the musical piece data are arranged to be produced at sound production timings thereof for each pitch range acquired by shifting a range of pitches corresponding to one octave by one semitone at each time are generated, and an accompaniment part is selected among them.

A digital tuner that determines a tuning target period; receives an audio signal from an instrument to be tuned; obtains a plurality of different segments of the audio signal starting at times that correspond to integer multiples of the target period; produces waveform samples from the segments; and displays of a succession of waveform segments at same display position using said segments so that the shape of the waveform appears to move on the display at a speed and direction directly dependent on a difference of a wave period of the audio signal to be tuned and the tuning target period.

A method for processing audio signals includes extracting a fundamental frequency (F0) component from a first audio signal; processing the first audio signal with Dominant Melody Enhancement (DoME) based on a hearing profile and output a second audio signal; and providing the second audio signal to the user. The DoME enhances the F0 component. The enhancement weight of the DoME is corresponding to the hearing profile.

Audio data of an original work is received. Text in the audio data is translated to a target language. The audio data is passed to a first deep learning model to learn voice features in the audio data. The audio data is passed to a second deep learning model to learn audio properties in the audio data. The translated text is synchronized to play in the position of original text of the original work in a synthesized voice. A translated audio data of the original work is created by combining the synchronized translated text in the synthesized voice with music of the audio data.

A computer-implemented sound signal generation method includes: obtaining a first sound source spectrum of a sound signal to be generated; obtaining a first spectral envelope of the sound signal; and estimating fragment data representative of samples of the sound signal based on the obtained first sound source spectrum and the obtained first spectral envelope.