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Found 9 papers, 6 papers with code
Pre-training Music Classification Models via Music Source Separation
In this paper, we study whether music source separation can be used as a pre-training strategy for music representation learning, targeted at music classification tasks.
Multi-Source Contrastive Learning from Musical Audio
Contrastive learning constitutes an emerging branch of self-supervised learning that leverages large amounts of unlabeled data, by learning a latent space, where pairs of different views of the same sample are associated.
Enhancing Affective Representations of Music-Induced EEG through Multimodal Supervision and latent Domain Adaptation
The study of Music Cognition and neural responses to music has been invaluable in understanding human emotions.
HTMD-Net: A Hybrid Masking-Denoising Approach to Time-Domain Monaural Singing Voice Separation
The advent of deep learning has led to the prevalence of deep neural network architectures for monaural music source separation, with end-to-end approaches that operate directly on the waveform level increasingly receiving research attention.
Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms
Sound Event Detection and Audio Classification tasks are traditionally addressed through time-frequency representations of audio signals such as spectrograms.
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition
Emotion Recognition from EEG signals has long been researched as it can assist numerous medical and rehabilitative applications.
Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music
Instrument classification is one of the fields in Music Information Retrieval (MIR) that has attracted a lot of research interest.
Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection
Detecting visual relationships, i. e. <Subject, Predicate, Object> triplets, is a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch.
Multimodal Visual Concept Learning with Weakly Supervised Techniques
Despite the availability of a huge amount of video data accompanied by descriptive texts, it is not always easy to exploit the information contained in natural language in order to automatically recognize video concepts.
