Lemon Inc.

Jitong Chen

Bochen Li

David Trevelyan

Tao Xiong

Maryyann Crichton

Fraser Smith

Nathanael Schager

Chenyu Sun

Lamtharn Hantrakul

Matthew David Avent

Josiah John Serrano

Hwankyoo Shawn Kim

Daiyu Zhang

Suiyu Feng

Vibert Thio

Haonan Chen

Rodrigo Castellon

Siyuan Shan

Xuefan Hu

Brandon Wu

Systems and methods directed to combining audio tracks are provided. More specifically, a first audio track and a second audio track are received. The first audio track is separated into a vocal component and one or more accompaniment components. The second audio track is separated into a vocal component and one or more accompaniment components. A structure of the first audio track and a structure of the second audio track are determined. The first audio track and the second audio track are aligned based on the determined structures of the tracks. The vocal component of the first audio track is stretched to match a tempo of the second audio track. The stretched vocal component of the first audio track is added to the one or more accompaniment components of the second audio track.

Automatic and interactive mashup system

Methods, systems, and storage media for generating a beatbox transcript are disclosed. Some examples may include: receiving an audio signal having a plurality of beatbox sounds, generating a spectrogram of the audio signal, processing the spectrogram of the audio signal with a neural network model trained on training samples including beatbox sounds, generating, by the neural network model a beatbox sound activation map including a plurality of activation times for a plurality of beatbox sounds, decoding the beatbox sound activation map into a beatbox transcript and providing the beatbox transcript as an output.

Beatboxing transcription

The present disclosure describes techniques for differentiable wavetable synthesizer. The techniques comprise extracting features from a dataset of sounds, wherein the features comprise at least timbre embedding; input the features to the first machine learning model, wherein the first machine learning model is configured to extract a set of N×L learnable parameters, N represents a number of wavetables, and L represents a wavetable length; outputting a plurality of wavetables, wherein each of plurality of wavetables comprises a waveform associated with a unique timbre, the plurality of wavetables form a dictionary, and the plurality of wavetables are portable to perform audio-related tasks. Finally, the said wavetables are used to initialize another machine learning model so as to help reduce computational complexity of an audio synthesis obtained as output of the another machine learning model.

Differentiable wavetable synthesizer using plurality of machine learning models to reduce computational complexity of audio synthesis

The present application provides a special effect processing method and apparatus. The method includes: generating an audio signal in response to a touch operation of a user in a process of playing a video; segmenting the audio signal into multiple audio frames; performing, according to attributes of the audio frames, special effect processing on a picture which is currently played in the video.

Special effect processing method and apparatus

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Los Angeles, CA, United States of America

Jitong Chen