music-3

Music Information Retrieval

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in music-information-retrieval-3

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FMA

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Most implemented papers

Music Artist Classification with Convolutional Recurrent Neural Networks

Yue Zhao, Zain Nasrullah•Sun Jan 13 2019

The results improve upon baseline works, verify the influence of the producer effect on classification performance and demonstrate the trade-offs between audio length and training set size.

50

Content

iKala

EMOPIA

Lakh Pianoroll Dataset

YouTube-100M

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Revisiting Singing Voice Detection: A quantitative review and the future outlook

Juhan Nam, Keunwoo Choi, Kyungyun Lee•Sun Jun 03 2018

This work performs an error analysis on three recent singing voice detection systems and designs novel methods to test the systems on multiple sets of internally curated and generated data to further examine the pitfalls, which are not clearly revealed with the current datasets.

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Optical Music Recognition with Convolutional Sequence-to-Sequence Models

Karen Ullrich, E. V. D. Wel•Sat Jul 15 2017

A deep learning architecture called a Convolutional Sequence-to-Sequence model is presented to both move towards an end- to-end trainable OMR pipeline, and apply a learning process that trains on full sentences of sheet music instead of individually labeled symbols.

64 0

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GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music

Jitong Chen, Qiuqiang Kong, Bochen Li, Yuxuan Wang•Sat Oct 10 2020

GiantMIDI-Piano is the largest classical piano MIDI dataset so far including the nationalities, the number and duration of works of composers, and the chroma, interval, trichord and tetrachord frequencies of six composers from different eras are shown to show that it can be used for musical analysis.

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Rethinking CNN Models for Audio Classification

Angela Yao, Dipika Singhania, Kamalesh Palanisamy•Tue Jul 21 2020

It is shown that ImageNet-Pretrained standard deep CNN models can be used as strong baseline networks for audio classification and qualitative results of what the CNNs learn from the spectrograms by visualizing the gradients are shown.

169 0

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Neural Audio Fingerprint for High-Specific Audio Retrieval Based on Contrastive Learning

Jeongsoon Park, Sungkyun Chang, Donmoon Lee, Hyungui Lim, Kyogu Lee, Karam Ko, Yoonchang Han•Wed Oct 21 2020

This work generates a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search, and presents a contrastive learning framework that derives from the segment-level search objective.

44 0

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A Tutorial on Deep Learning for Music Information Retrieval

Kyunghyun Cho, Keunwoo Choi, György Fazekas, Mark B. Sandler•Tue Sep 12 2017

The basic principles and prominent works in deep learning for MIR are laid out and the network structures that have been successful in MIR problems are outlined to facilitate the selection of building blocks for the problems at hand.

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Singing voice separation using a deep convolutional neural network trained by ideal binary mask and cross entropy

Kin Wah Edward Lin, T. BalamuraliB., Enyan Koh, Simon Lui, Dorien Herremans•Mon Dec 03 2018

A unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification is presented, which is combined with cross entropy loss and pretraining of the CNN as an autoencoder on singing voice spectrograms.

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Learning a Representation for Cover Song Identification Using Convolutional Neural Network

Xiaoou Chen, Deshun Yang, Zhesong Yu, Xiaoshuo Xu•Thu Oct 31 2019

A novel Convolutional Neural Network is proposed towards cover song identification that outperforms state-of-the-art methods on several public datasets with low time complexity.

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Improving Machine Hearing on Limited Data Sets

P. Harár, Roswitha Bammer, Anna Breger, M. Dörfler, Z. Smékal•Wed Mar 20 2019

This contribution investigates how input and target representations interplay with the amount of available training data in a music information retrieval setting and compares the standard mel-spectrogram inputs with a newly proposed representation, called Mel scattering.

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