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.

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.

Methods and apparatus to classify media based on a pitch-independent timbre attribute from a media signal are disclosed. An example apparatus includes means for accessing a media signal; and means for: determining a spectrum of audio corresponding to the media signal; and determining a timbre-independent pitch attribute of audio of the media signal based on an inverse transform of a complex argument of a transform of the spectrum.

An intelligent system for matching audio with video of the present invention provides a video analysis module targeting color tone, storyboard pace, video dialogue, length and category and director's special requirement, actors expression, movement, weather, scene, buildings, spacial and temporal, things and a music analysis module targeting recorded music form, sectional turn, style, melody and emotional tension, and then uses an AI matching module to adequately match video of the video analysis module with musical characteristics of the music analysis module, so as to quickly complete a creative composition selection function with respect to matching audio with a video.

Disclosed in the embodiments of the present disclosure are an audio playback method and apparatus, a computer readable storage medium, and an electronic device. The method includes: acquiring intention determination data collected for at least one user within a target space; determining that at least one user has a target vocal intention based on the intention determination data, and then determining feature information representing a current feature of the at least one user; and extracting and playing an audio corresponding to the feature information from a preset audio library. According to the embodiments of the present disclosure, it is achieved to automatically perform a determination on the target vocal intention of the user by the electronic device, without triggering an audio playing operation by the user, the steps of performing the audio playing operation by the user being omitted, and the convenience of the audio playing operation being improved. In addition, by determining the current feature of the user, the played audio is adapted to the feature of the user so as to achieve an effect of more accurately playing the audio which the user wants to listen to and improve pertinence of automatic playback of the audio.

The present invention relates to a method and a device 10 for processing mixed audio data, including decomposing in real-time with low latency, in which a continuous stream of mixed audio data is received from an audio source 14, a first chunk of the stream of mixed audio data is loaded into a buffer, the audio data contained in the buffer is decomposed to obtain first decomposed audio data representing audio signals of a predetermined timbre, and a first chunk of output data is obtained from the first decomposed audio data, preferably for direct playback via speaker 26.

A method for automatically generating an audio signal, the method comprising receiving a source audio signal analyzing the source audio signal to identify a musical parameter characteristic thereof obtaining a supplemental audio signal based on the identified musical parameter characteristic and combining the source audio signal and the supplemental audio signal to form an extended audio signal.

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.

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 music beat point detection method includes: performing a frame processing on a music signal to obtain a frame signal; obtaining a power spectrum of the frame signal; performing sub-band decomposition on the power spectrum, and decomposing the power spectrum into at least two sub-bands; performing a time-frequency domain joint filtering on a signal of each sub-band according to a beat type corresponding to each sub-band; obtaining a to-be-confirmed beat point from the frame signal of the music signal according to a result of the time-frequency domain joint filtering; and obtaining a beat point of the music signal according to a power value of the to-be-confirmed beat point.