20 papers with code • 0 benchmarks • 8 datasets
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Recently, the end-to-end approach that learns hierarchical representations from raw data using deep convolutional neural networks has been successfully explored in the image, text and speech domains.
We introduce CLaMP: Contrastive Language-Music Pre-training, which learns cross-modal representations between natural language and symbolic music using a music encoder and a text encoder trained jointly with a contrastive loss.
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.
Deep convolutional neural networks (CNNs) have been actively adopted in the field of music information retrieval, e. g. genre classification, mood detection, and chord recognition.
Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification
Music tag words that describe music audio by text have different levels of abstraction.
Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels.