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
Music Artist Classification with Convolutional Recurrent Neural Networks
ZainNasrullah/music-artist-classification-crnn
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To this end, an established classification architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to the artist20 music artist identification dataset under a comprehensive set of conditions.
Revisiting Singing Voice Detection: a Quantitative Review and the Future Outlook
Since the vocal component plays a crucial role in popular music, singing voice detection has been an active research topic in music information retrieval.
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
This data set is the first publicly available set in OMR research with sufficient size to train and evaluate deep learning models.
Rethinking CNN Models for Audio Classification
kamalesh0406/Audio-Classification
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Besides, we show that even though we use the pretrained model weights for initialization, there is variance in performance in various output runs of the same model.
GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music
In this article, we create a GiantMIDI-Piano (GP) dataset containing 38, 700, 838 transcribed notes and 10, 855 unique solo piano works composed by 2, 786 composers.
Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning
Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale.
A Tutorial on Deep Learning for Music Information Retrieval
Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research.
Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy
EdwardLin2014/CNN-with-IBM-for-Singing-Voice-Separation
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We present a unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification, which we combine with cross entropy loss and pretraining of the CNN as an autoencoder on singing voice spectrograms.
Improving Machine Hearing on Limited Data Sets
In this contribution we investigate how input and target representations interplay with the amount of available training data in a music information retrieval setting.
Learning a Representation for Cover Song Identification Using Convolutional Neural Network
We first train the network through classification strategies; the network is then used to extract music representation for cover song identification.
