A method for processing an audio signal and an electronic device, relate to the field of audio and video technology. The method includes: detecting beat information of the audio signal; and obtaining virtual surround sound for the audio signal by performing a convolution operation on a head-related transfer function and the audio signal based on the beat information of the audio signal.

A system and a method for synchronizing performance effects with a musical performance. The method includes receiving a MIDI signal representing an audio track currently being played; determining, in near real-time, a tempo value of the said audio track based on a corresponding clock speed information as obtained from the MIDI signal; receiving information about a video file selected to be played along with the said audio track; determining, in near real-time, a tempo value of the said video file based on the received information thereabout; processing the said video file to determine required playback speed adjustment therefor to cause the tempo value of the said video to be in sync with the determined tempo value of the said audio track; and playing the said video file with the required playback speed adjustment applied thereto, along with the audio track in the musical performance.

The present application discloses and describes neurostimulation systems and methods that include, among other features, (i) neural stimulation through audio with dynamic modulation characteristics, (ii) audio content serving and creation based on modulation characteristics, (iii) extending audio tracks while avoiding audio discontinuities, and (iv) non-auditory neurostimulation and methods, including non-auditory neurostimulation for anesthesia recovery.

Provided is a method for processing audio including: acquiring an accompaniment audio signal and a voice signal of a current to-be-processed musical composition; determining a target reverberation intensity parameter value of the acquired accompaniment audio signal, wherein the target reverberation intensity parameter value is configured to indicate a rhythm speed, an accompaniment type, and a performance score of a singer of the current to-be-processed musical composition; and reverberating the acquired vocal signal based on the target reverberation intensity parameter value.

Techniques are disclosed relating to implementing audio techniques for real-time audio generation. For example, a music generator system may generate new music content from playback music content based on different parameter representations of an audio signal. In some cases, an audio signal can be represented by both a graph of the signal (e.g., an audio signal graph) relative to time and a graph of the signal relative to beats (e.g., a signal graph). The signal graph is invariant to tempo, which allows for tempo invariant modification of audio parameters of the music content in addition to tempo variant modifications based on the audio signal graph.

A system, method and computer product for combining audio tracks. In one example embodiment herein, the method comprises determining at least one music track that is musically compatible with a base music track, aligning those tracks in time, and combining the tracks. In one example embodiment herein, the tracks may be music tracks of different songs, the base music track can be an instrumental accompaniment track, and the at least one music track can be a vocal track. Also in one example embodiment herein, the determining is based on musical characteristics associated with at least one of the tracks, such as an acoustic feature vector distance between tracks, a likelihood of at least one track including a vocal component, a tempo, or musical key. Also, determining of musical compatibility can include determining at least one of a vertical musical compatibility or a horizontal musical compatibility among tracks.

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 present invention provides a method for processing audio data, comprising the steps of providing input audio data containing a mixture of audio data including first audio data of a first musical timbre and second audio data of a second musical timbre different from said first musical timbre, decomposing the input audio data to provide decomposed data representative of the first audio data, transforming the decomposed data to obtain third audio data.

A device to determine a number of beats per bar from a music data includes at least one processor configured to calculate a weighted average beat level waveform from a first beat level waveform obtained for a first frequency band and a second beat level waveform obtained for a second frequency band; calculate autocorrelation on the weighted average beat level waveform by varying an amount of a shift interval for the autocorrelation; determine a plurality of the shift intervals at which correlation values of the autocorrelation are n highest, where n is a positive integer greater than or equal to 2; and determine the number of beats per bar based on the determined plurality of the shift intervals at which the correlation values of the autocorrelation are n highest.

Devices, systems, and methods related to implementing supervised metric learning during a training of a deep neural network model are disclosed herein. In examples, audio input may be received, where the audio input includes a plurality of song fragments from a plurality of songs. For each song fragment, an aligning function may be performed to center the song fragment based on determined beat information, thereby creating a plurality of aligned song fragments. For each song fragment of the plurality of song fragments, an embedding vector may be obtained from the deep neural network. Thus, a batch of aligned song fragments from the plurality of aligned song fragments may be selected, such that a training tuple may be selected. A loss metric may be generated based on the selected training tuple and one or more weights of the deep neural network model may be updated based on the loss metric.