A music piece analyzer includes: a beat-position-acquiring-unit configured to detect beat positions in music piece data; a snare drum detector configured to detect sounding positions of a snare drum in the music piece data; a bass drum detector configured to detect sounding positions of a bass drum in, the music piece data; a one-beat-shift-determination-unit configured to determine whether a bar beginning of the music piece data is shifted by one beat based upon the sounding positions of the snare drum detected by the snare drum detector; a two-beat-shift-determination-unit configured to determine whether the bar beginning of the music piece data is shifted by two beats on a basis of the sounding positions of the bass drum detected by the bass drum detector; and a bar-beginning-setting-unit configured to set the bar beginning of the music piece data on a basis of results determined by the one-beat-shift-determination-unit and the two-beat-shift-determination-unit.

A media-playback device acquires a heart rate, selects a song with a first tempo, and initiates playback of the song. The song meets a set of qualification criteria and the first tempo is based on the heart rate, such as being equal to or less than the heart rate. The media-playback device also initiates playback of a binaural beat at a first frequency. Over a period of time, the binaural beat's first frequency is changed to a second frequency. Over the period of time, the first tempo can also be changed to a second tempo, where the second tempo is slower than the first tempo.

The present invention relates to a method for processing and playing audio data comprising the steps of receiving mixed input data and playing recombined output data. Furthermore, the invention relates to a device for processing and playing audio data, preferably DJ equipment, comprising an audio input unit for receiving a mixed input signal, a recombination unit and a playing unit for playing recombined output data. In addition, the present invention relates to a method and a device for representing audio data, i.e. on a display.

The disclosure provides a method for generating action according to an audio signal and an electronic device. The method includes: receiving an audio signal and extracting a high-level audio feature therefrom; extracting a latent audio feature from the high-level audio feature; in response to determining that the audio signal corresponds to a beat, obtaining a joint angle distribution matrix based on the latent audio feature; in response to determining that the audio signal corresponds to a music, obtaining a plurality of designated joint angles corresponding to a plurality of joint points based on the joint angle distribution matrix; and adjusting a joint angle of each of the joint points on the avatar according to the designated joint angles.

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.

Systems and methods for feature-based music selection may include (1) receiving user input selecting a music composition, (2) identifying features of the music composition including (i) a musical feature, relating to a musical quality of the music composition and (ii) a lyrical feature, relating to one or more of the music composition's lyrics, (3) determining that an additional music composition is similar to the music composition based on a comparison of the features of the music composition with features of the additional music composition, and (4) selecting the additional music composition to be added to a queue associated with the music composition based on the determination. Various other methods, systems, and computer-readable media are also disclosed.

A media-playback device acquires a heart rate, selects a song with a first tempo, and initiates playback of the song. The song meets a set of qualification criteria and the first tempo is based on the heart rate, such as being equal to or less than the heart rate. The media-playback device also initiates playback of a binaural beat at a first frequency. Over a period of time, the binaural beat's first frequency is changed to a second frequency. Over the period of time, the first tempo can also be changed to a second tempo, where the second tempo is slower than the first tempo.

A system and method for confirming a voice command of a media playback device is disclosed. The method includes receiving an instruction of a voice command and producing an audio confirmation of the command. A confirmation may be playing a media context item associated with the command, playing a verbal confirmation phrase, or playing a non-verbal audio cue.

A beat timing generation device including a generation unit for generating, from data of music that has been input, information of timings on which a beat of the music depends and a plurality of pieces of intensity data indicating a power at the timings; a calculation unit for calculating a period and a phase of the beat of the music using the plurality of pieces of intensity data; and a detection unit for detecting a beat generation timing on a basis of the period and the phase of the beat.

A system for generation of a musical notation from an audio signal, the system comprising at least one processor configured to: obtain the audio signal from an audio source or a data repository; process the audio signal using first machine learning (ML) model(s) to generate a recognition result, wherein the recognition result is indicative of a pitch and a duration of a plurality of notes in the audio signal and their corresponding confidence scores; generate a preliminary musical notation using the recognition result; process the preliminary musical notation using second ML model(s) to determine whether the preliminary musical notation includes one or more errors; and when it is determined that the preliminary musical notation includes one or more errors, modify the preliminary musical notation to generate the musical notation that is error-free or has lesser errors as compared to the preliminary musical notation.