NSF Projects

III: Small: Collaborative Research: Algorithms for Query by Example of Audio Databases

BIGDATA: F: Audio-Visual Scene Understanding

CAREER: Human-Computer Collaborative Music Making


Music


Don't Look Back: An Online Beat Tracking Method Using RNN and Enhanced Particle Filtering

A particle filtering approach to online beat tracking based on beat activiation values calculated by a recurrent neural network.



FolkDuet: When Counterpoint Meets Chinese Folk Melodies

A deep reinforcement learning method that tranfers counterpoint patterns from J.S. Bach Chorales to compose countermelodies for Chinese folk melodies.



BachDuet: Human-Machine Counterpoint Improvisation

A deep learning system that allows a human musician to improvise a duet counterpoint with a machine partner in real time. We hope that this system will help revitalize the improvisation culture in classical music education and performance!



Online Music Accompaniment by Reinforcement Learning

We propose a reinforcement learning framework for online music accompaniment in the style of Western counterpoint. The reward model is trained from J.S. Bach chorales to model intra- and inter-part interaction.



Automatic Music Composition

Generate music compositions automatically in a musically plausible way.



Skeleton Plays Piano: Generating Pianist Movement from MIDI Data

We train a model to take the input of MIDI data, and output the visual performance as expressive body movements for pianist. It can be used for demonstration purpose for music learners, or immersive music enjoyment system, or human-computer interactions in automatic accompaniment systems. We show all the demo videos of the generated visual performance (as skeleton key points) compared with real human on same pieces.



Piano Music Transcription Into Music Notation

A complete piano music transcription system from transcribing notes from audio waveform to arranging as readable score notations





Creating A Musical Performance Dataset For Multimodal Music Analysis

We create an audio-visual, multi-track, and multi-instrument music performance dataset that comprises a number of chamber music assembled from coordinated but separately recorded performances of individual tracks. With ground-truth pitch/note annotations and clean individual audio tracks available, this can be used for multi-modal analysis of music performance.




The URSing Dataset

We introduce a dataset for facilitating audio-visual analysis of singing performances. The dataset comprises a number of singing performances as audio and video recordings. Each song contains the isolated track of solo singing voice and the mixure with accompaniment track. We anticipate that the dataset will be useful for multi-modal analysis of singing performances, such as audiovisual singing voice separation, and serve as ground-truth for evaluations.



Score Following For Expressive Piano Performance

We address the "sustained effect" in piano music performance, caused by the usage of sustained pedal or legato articulations. Due to this effect, the mixture of energy between the sustained and following notes (non-notated in the score) always results in delay erros in score following systems. We propose to modify the audio feature representations to reduce the sustained effect and enhance the robustness of score following systems.



Automatic Lyrics Display For A Live Chorus Performance

Live musical performances (e.g., choruses, concerts, and operas) often require the display of lyrics for the convenience of the audience. We propose a computational system to automate this real-time lyrics display process using signal processing techniques




Audio-visual Analysis of Music Performance

We propose to leverage visual information captured from music performance videos to advance several music information retrieval (MIR) tasks, such as source association, multi-pitch analysis, and vibrato analysis.







Speech


Speech Driven Talking Face Generation from a Single Image and an Emotion Condition

We propose an end-to-end talking face generation system that can take a speech utterance, a face image, and an emotion condition (e.g., happy, angry, etc.) as input, to render a talking face expressing that emotion.



End-to-End Generation of Talking Faces from Noisy Speech

We propose a system that can generate talking faces from input noisy speech and a reference image.



Generating 3D Talking Face Landmarks from Speech

We propose to use a Convolutional network to generate 3D landmarks of a talking face from acoustic speech waveform.



Adversarial Training for Speech Super-Resolution

We propose an adversarial training method for speech super-resolution or speech bandwidth extension.



Audio-Visual Speech Source Separation

we propose an audio-visual Audio-Visual Deep Clustering model (AVDC) to integrate visual information into the process of learning better feature representations (embeddings) for Time-Frequency (T-F) bin clustering.



Generating Talking Face Landmarks from Speech

We propose to use a LSTM network to generate landmarks of a talking face from acoustic speech.





General Sounds


Sound Search By Vocal Imitation

We propose to make general audio databases content-searchable using vocal imitation of the desired sound as the query key: A user vocalizes the audio concept in mind and the system retrieves audio recordings that are similar, in some way, to the vocalization.




Others


Prior Projects