An apparatus is provided that accurately estimates a point at which a musical performance is started by a player. The apparatus includes the musical performance analysis unit 32, and the musical performance analysis unit 32 obtains action data that includes a time series of feature data representing actions made by a player during a musical performance for a reference period and estimating a sound-production point based on the action data at an estimated point using a learned model L.

A method relating to synchronize MIDI file with video includes acquiring a video and a MIDI file, and identifying timing of a video frame. The method also includes converting timing into tick information and editing a tick of the MIDI file. The method further includes detecting the MIDI file corresponding to the video frame, and playing a musical instrument based on the MIDI file corresponding to the video.

Due to discrepancies in musical timing signatures, the invention assesses whether a recorded displacement, expressed in terms of beats and fractions, between exit and entry points for a potential musical splice or cut, corresponds to permit a seamless music splicing of different musical sections. Assessment is achieved by establishing a third time base of pulses having a length dependent upon a lowest common multiple of fractions within respective bars for different sections, with the bars of the respective sections then partitioned into an equal number of fixed length pulses. A coefficient aligns different time signatures; it is a ratio between pulses within the different sections. The coefficient identifies corresponding locations of a cut point, related to a suitable anacrusis, in terms of respectively an aligned bar, beat, quaver and fraction in differing time signatures. The coefficient ensures that the time anacrusis in one time signature is interchangeable with others.

Disclosed are a system and a method for singing expression transplantation. A singing expression transplantation method performed by a singing expression transplantation system according to an embodiment may comprise the steps of: synchronizing each of a first sound source and a second sound source, which include different pieces of voice information with regard to an identical song; modifying the pitch of the first sound source on the basis of pitch information extracted from each of the first sound source and the second sound source, which have been synchronized; and extracting volume information from each of the first sound source and the second sound source and adjusting the magnitude of the volume regarding the first sound source, the pitch of which has been modified, according to each piece of extracted volume information.

A dual sound source audio data processing method and apparatus are provided. The method includes obtaining audio data of a song pair including a first song and a second song, the first and second songs having a same accompaniment audio but different voice audio. The audio data is decoded to obtain first mono audio data corresponding to the first song and second mono audio data corresponding to the second song. The first and second mono audio data are combined to one piece of two-channel audio data including a left audio channel and a right audio channel. A play time of the two-channel audio data is divided into play periods, and energy suppression is selectively performed on the left audio channel and the right audio channel in different play periods.

A computer implemented method for analysing sounds, such as audio tracks, and automatically classifying the sounds in a space in which arousal is one axis and valence is another axis. The location of a sound or track in that arousal-valence space is automatically determined using a computer implemented system that analyses, measures or infers values for each of the following base feature parameters: harmonicity, turbulence, rhythmicity, sharpness, volume and linear harmonic cost, or any combination of two or more of those parameters.

A lyrics analyzer generates tags and explicitness indicators for a set of tracks. These tags may indicate the genre, mood, occasion, or other features of each track. The lyrics analyzer does so by generating an n-dimensional vector relating to a set of topics extracted from the lyrics and then using those vectors to train a classifier to determine whether each tag applies to each track. The lyrics analyzer may also generate playlists for a user based on a single seed song by comparing the lyrics vector or the lyrics and acoustics vectors of the seed song to other songs to select songs that closely match the seed song. Such a playlist generator may also take into account the tags generated for each track.

Techniques for automatically controlling the expressive behavior of a virtual musical instrument by analyzing an audio recording of a live musician are provided. In some embodiments, an audio recording may be analyzed at various points along the timeline of the recording to derive corresponding values of a parameter that is in some way representative of the musical expression of the live musician. Values of control parameters that control one or more aspects of the audio playback of a virtual instrument may then be generated based on the determined values of the expression parameter. Values of control parameters may be provided to a sample library to control how a digital score selects and/or plays back samples from the library, and/or values of the control parameters may be stored with the digital score for subsequent playback.

A system and method for estimating the popularity of song by calculating the (absolute and/or contrastive) harmonic surprise of each song in a corpus of music data, determining the popularity of each song in the corpus (e.g., based on a music chart, downloads, online streams), determining correlations between harmonic surprise and popularity, and estimating the popularity of an individual song based on the (absolute and/or contrastive) harmonic surprise of the individual song and the correlations between harmonic surprise and popularity.

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.