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Found 15 papers, 9 papers with code
LP-MusicCaps: LLM-Based Pseudo Music Captioning
In addition, we trained a transformer-based music captioning model with the dataset and evaluated it under zero-shot and transfer-learning settings.
Pseudo-Label Transfer from Frame-Level to Note-Level in a Teacher-Student Framework for Singing Transcription from Polyphonic Music
We address the issue by using pseudo labels from vocal pitch estimation models given unlabeled data.
Disentangled Multidimensional Metric Learning for Music Similarity
For this task, it is typically necessary to define a similarity metric to compare one recording to another.
Metric Learning vs Classification for Disentangled Music Representation Learning
For this, we (1) outline past work on the relationship between metric learning and classification, (2) extend this relationship to multi-label data by exploring three different learning approaches and their disentangled versions, and (3) evaluate all models on four tasks (training time, similarity retrieval, auto-tagging, and triplet prediction).
Musical Word Embedding: Bridging the Gap between Listening Contexts and Music
Word embedding pioneered by Mikolov et al. is a staple technique for word representations in natural language processing (NLP) research which has also found popularity in music information retrieval tasks.
Zero-shot Learning for Audio-based Music Classification and Tagging
Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels.
Representation Learning of Music Using Artist, Album, and Track Information
Supervised music representation learning has been performed mainly using semantic labels such as music genres.
Zero-shot Learning and Knowledge Transfer in Music Classification and Tagging
Music classification and tagging is conducted through categorical supervised learning with a fixed set of labels.
Deep Content-User Embedding Model for Music Recommendation
Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach.
Raw Waveform-based Audio Classification Using Sample-level CNN Architectures
Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics.
Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms
Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks.
Representation Learning of Music Using Artist Labels
In this paper, we present a supervised feature learning approach using artist labels annotated in every single track as objective meta data.
Sound Audio and Speech Processing
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.
Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-tagging
Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip.
Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms
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.
