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Found 6 papers, 1 papers with code
Interpretable Melody Generation from Lyrics with Discrete-Valued Adversarial Training
Generating melody from lyrics is an interesting yet challenging task in the area of artificial intelligence and music.
MusicTM-Dataset for Joint Representation Learning among Sheet Music, Lyrics, and Musical Audio
This work present a music dataset named MusicTM-Dataset, which is utilized in improving the representation learning ability of different types of cross-modal retrieval (CMR).
Unsupervised Generative Adversarial Alignment Representation for Sheet music, Audio and Lyrics
In this paper, we propose an unsupervised generative adversarial alignment representation (UGAAR) model to learn deep discriminative representations shared across three major musical modalities: sheet music, lyrics, and audio, where a deep neural network based architecture on three branches is jointly trained.
Audio-Visual Embedding for Cross-Modal MusicVideo Retrieval through Supervised Deep CCA
ii) We propose an end-to-end deep model for cross-modal audio-visual learning where S-DCCA is trained to learn the semantic correlation between audio and visual modalities.
Personalized Music Recommendation with Triplet Network
Since many online music services emerged in recent years so that effective music recommendation systems are desirable.
Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-modal Retrieval
In particular, two significant contributions are made: i) a better representation by constructing deep triplet neural network with triplet loss for optimal projections can be generated to maximize correlation in the shared subspace.
