An accompaniment classification method and apparatus is provided. The method includes the following. A first type of audio features of a target accompaniment is obtained (S301, S401). Data normalization is performed on each kind of audio features in the first type of audio features of the target accompaniment to obtain a first feature-set of the target accompaniment and the first feature-set is input into a first classification model for processing (S302, S402). A first probability value output by the first classification model for the first feature-set is obtained (S303, S403). An accompaniment category of the target accompaniment is determined to be a first category of accompaniments when the first probability value is greater than a first classification threshold (S404). The accompaniment category of the target accompaniment is determined to be other categories of accompaniments when the first probability value is less than or equal to the first classification threshold.

A computer extracts a vocal portion from a first audio content item and determines a first representative vector that corresponds to a vocal style of the first audio content item by applying a variational autoencoder (VAE) to the extracted vocal portion of the representation of the audio content item. The computer streams, to an electronic device, a second audio content item, selected from a plurality of audio content items, that has a second representative vector that corresponds to a vocal style of the second audio content item, wherein the second representative vector that corresponds the vocal style of the second audio content item meets similarity criteria with respect to the first representative vector that corresponds to the vocal style of the first audio content item.

Systems, methods, and non-transitory computer-readable media can be configured to obtain source audio based on recorded audio. A tuned audio transform can be generated based on a source audio transform corresponding to the source audio and a recorded audio transform corresponding to the recorded audio. Tuned audio can be generated based on the tuned audio transform.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using complex linear projection are disclosed. In one aspect, a method includes the actions of receiving audio data corresponding to an utterance. The method further includes generating frequency domain data using the audio data. The method further includes processing the frequency domain data using complex linear projection. The method further includes providing the processed frequency domain data to a neural network trained as an acoustic model. The method further includes generating a transcription for the utterance that is determined based at least on output that the neural network provides in response to receiving the processed frequency domain data.

A method according to one aspect of the present disclosure includes acquiring verbal data representing a verbal expression corresponding to a sound reproduced by an acoustic device, and displaying, on a display device, motion graphics including the verbal expression corresponding to the sound reproduced by the acoustic device in a form of a text in accordance with the verbal data. The displaying the motion graphics on the display device includes selecting a type of motion graphics that relates to the verbal expression corresponding to the reproduced sound from among various types of motion graphics and displaying the selected type of motion graphics on the display device.

The disclosure provides a method and an apparatus for song generation. A text input may be received. A topic and an emotion may be extracted from the text input. A melody may be determined according to the topic and the emotion. Lyrics may be generated according to the melody and the text input. A song may be generated at least according to the melody and the lyrics.

In implementations of searching for music, a music search system can receive a music search request that includes a music file including music content. The music search system can also receive a selected musical attribute from a plurality of musical attributes. The music search system includes a music search application that can generate musical features of the music content, where a respective one or more of the musical features correspond to a respective one of the musical attributes. The music search application can then compare the musical features that correspond to the selected musical attribute to audio features of audio files, and determine similar audio files to the music file based on the comparison of the musical features to the audio features of the audio files.

The invention provides for the evaluation of semantic closeness of a source data file relative to candidate data files. The system includes an artificial neural network and processing intelligence that derives a property vector from extractable measurable properties of a data file. The property vector is mapped to related semantic properties for that same data file and such that, during ANN training, pairwise similarity/dissimilarity in property is mapped, during towards corresponding pairwise semantic similarity/dissimilarity in semantic space to preserve semantic relationships. Based on comparisons between generated property vectors in continuous multi-dimensional property space, the system and method assess, rank, and then recommend and/or filter semantically close or semantically disparate candidate files from a query from a user that includes the data file. Applications of the categorization and recommendation system apply to search tools, including identification of illicit materials or logically progressive associations between disparate files.

The invention provides for the evaluation of semantic closeness of a source data file relative to candidate data files. The system includes an artificial neural network and processing intelligence that derives a property vector from extractable measurable properties of a data file. The property vector is mapped to related semantic properties for that same data file and such that, during ANN training, pairwise similarity/dissimilarity in property is mapped, during towards corresponding pairwise semantic similarity/dissimilarity in semantic space to preserve semantic relationships. Based on comparisons between generated property vectors in continuous multi-dimensional property space, the system and method assess, rank, and then recommend and/or filter semantically close or semantically disparate candidate files from a query from a user that includes the data file. Applications apply to search and compilation tools and particularly to recommendation tools that provide a succession of logical progressive associations that link between disparate file content in source and destination files.

A property vector derived from extractable measurable properties of a data file is mapped to semantic properties for that data file. The property vector is an output from a trained artificial neural network that, following pairwise training of the ANN using pairs of files that map pairwise similarity/dissimilarity in property space towards corresponding pairwise semantic similarity/dissimilarity in semantic space, both preserves and is representative of semantic properties of the data file. The system and method assesses, based on comparisons between generated property vectors, ranks and then recommends and/or filters semantically close or semantically disparate candidate files in a database from a query from a user that includes the data file. Applications of the categorization and recommendation system and method apply to media or search tools and social media platforms, including media in the form of music, video, images data and/or text files.