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Name: Audio, Music, and AI Lab at SUTD
Type: Organization
Name: Audio, Music, and AI Lab at SUTD
Type: Organization
This is a list of datasets consisting of speech, music, and sound effects, which can provide training data for Generative AI, AIGC, AI model training, intelligent audio tool development, and audio applications. It is mainly used for speech recognition, speech synthesis, singing voice synthesis, music information retrieval, music generation, etc.
Allan Lab website
PyTorch Dataset for Speech and Music audio
A curated list of Datasets, Models and Papers for Music Emotion Recognition (MER)
Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-term Structure
A variational autoencoder for music generation with tension control
Conditional Drums Generation using Compound Word Representations
Conditional VAE for Accented Speech Generation
This repository collects information about different data sets for Music Emotion Recognition.
Demucs Lightning: A PyTorch lightning version of Demucs with Hydra and Tensorboard features
PyTorch implementation of DiffRoll, a diffusion-based generative automatic music transcription (AMT) model
Resources for DisfluencySpeech
Website emotion guidance
Fundamental Music Embedding, FME
a list of demo websites for automatic music generation research
An exploration of how generative text-to-music AI models can be used for emotion guidance
Regression-based Music Emotion Prediction using Triplet Neural Networks
Official Implementation of Jointist
Web interface for AI music generation models
A novel seq2seq framework where high-level musicalities (such us the valence of the chord progression) are fed to the Encoder, and they are "translated" to lead sheet events in the Decoder. For further details please read and cite our paper:
A large-scale dataset of caption-annotated MIDI files.
music inpainting control
Code accompanying ISMIR 2020 paper - "Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling".
Mustango: Toward Controllable Text-to-Music Generation
Predicting emotion from music videos: exploring the relative contribution of visual and auditory information on affective responses
Audio processing by using pytorch 1D convolution network
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