Music Information Retrieval
94 papers with code • 0 benchmarks • 23 datasets
Benchmarks
These leaderboards are used to track progress in Music Information Retrieval
Datasets
FMA
MedleyDB
RWC
URMP
EMOPIA
YouTube-100M
Most implemented papers
Explaining Deep Convolutional Neural Networks on Music Classification
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.
Lyrics-Based Music Genre Classification Using a Hierarchical Attention Network
In this study we apply recurrent neural network models to classify a large dataset of intact song lyrics.
One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies
eldrin/MTLMusicRepresentation-PyTorch
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In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain.
CREPE: A Convolutional Representation for Pitch Estimation
marl/crepe
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To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics.
Lyrics Segmentation: Textual Macrostructure Detection using Convolutions
TuringTrain/lyrics_segmentation
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COLING 2018
Lyrics contain repeated patterns that are correlated with the repetitions found in the music they accompany.
Large-Scale Cover Song Detection in Digital Music Libraries Using Metadata, Lyrics and Audio Features
In this work, we investigate whether textual music information (such as metadata and lyrics) can be used along with audio for large-scale cover identification problem in a wide digital music library.
The Music Streaming Sessions Dataset
yunhak0/muse
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In order to spur that research, we release the Music Streaming Sessions Dataset (MSSD), which consists of 160 million listening sessions and associated user actions.
A Unified Neural Architecture for Instrumental Audio Tasks
Within Music Information Retrieval (MIR), prominent tasks -- including pitch-tracking, source-separation, super-resolution, and synthesis -- typically call for specialised methods, despite their similarities.
Recognizing Musical Entities in User-generated Content
Recognizing Musical Entities is important for Music Information Retrieval (MIR) since it can improve the performance of several tasks such as music recommendation, genre classification or artist similarity.
Deep Learning for Audio Signal Processing
johnhernandez3/VANGOGHS-EAR
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Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing.
