Music classification: Beyond supervised learning, towards real-world applications
Music classification is a music information retrieval (MIR) task to classify music items to
labels such as genre, mood, and instruments. It is also closely related to other concepts such …
labels such as genre, mood, and instruments. It is also closely related to other concepts such …
Pre-training strategies using contrastive learning and playlist information for music classification and similarity
In this work, we investigate an approach that relies on contrastive learning and music
metadata as a weak source of supervision to train music representation models. Recent …
metadata as a weak source of supervision to train music representation models. Recent …
Music classification using an improved crnn with multi-directional spatial dependencies in both time and frequency dimensions
In music classification tasks, Convolutional Recurrent Neural Network (CRNN) has achieved
state-of-the-art performance on several data sets. However, the current CRNN technique …
state-of-the-art performance on several data sets. However, the current CRNN technique …
Machine translation in low-resource languages by an adversarial neural network
Existing Sequence-to-Sequence (Seq2Seq) Neural Machine Translation (NMT) shows
strong capability with High-Resource Languages (HRLs). However, this approach poses …
strong capability with High-Resource Languages (HRLs). However, this approach poses …
Learning to recognize musical genre from audio: Challenge overview
Learning to Recognize Musical Genre from Audio Page 1 Learning to Recognize Musical
Genre from Audio Challenge Overview Michaël Defferrard EPFL, Lausanne, Switzerland …
Genre from Audio Challenge Overview Michaël Defferrard EPFL, Lausanne, Switzerland …
Efficient supervised training of audio transformers for music representation learning
In this work, we address music representation learning using convolution-free transformers.
We build on top of existing spectrogram-based audio transformers such as AST and train our …
We build on top of existing spectrogram-based audio transformers such as AST and train our …
Knowledge-graph augmented music representation for genre classification
In this paper, we propose KGenre, a knowledge-embedded music representation learning
framework for improved genre classification. We construct the knowledge graph from the …
framework for improved genre classification. We construct the knowledge graph from the …
An effective analysis of deep learning based approaches for audio based feature extraction and its visualization
Dhiraj, R Biswas, N Ghattamaraju - Multimedia Tools and Applications, 2019 - Springer
Visualizations help decipher latent patterns in music and garner a deep understanding of a
song's characteristics. This paper offers a critical analysis of the effectiveness of various …
song's characteristics. This paper offers a critical analysis of the effectiveness of various …
Genre Classification Empowered by Knowledge-Embedded Music Representation
This paper introduces a pioneering framework for music representation learning, which
harnesses knowledge graph embeddings to enrich genre classification. Leveraging …
harnesses knowledge graph embeddings to enrich genre classification. Leveraging …
Transfer learning with deep neural embeddings for music classification tasks
In this paper we present an approach for transfer learning with deep neural embeddings
applied to a selection of music information retrieval (MIR) classification tasks with several …
applied to a selection of music information retrieval (MIR) classification tasks with several …