music-10

Music Genre Recognition

3260 papers • 126 benchmarks • 313 datasets

Recognizing the genre (e.g. rock, pop, jazz, etc.) of a piece of music.

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Most implemented papers

Learning Discrete Structures for Graph Neural Networks

Mathias Niepert, M. Pontil, Luca Franceschi, Xiaojuan He•Wed Mar 27 2019

This work proposes to jointly learn the graph structure and the parameters of graph convolutional networks (GCNs) by approximately solving a bilevel program that learns a discrete probability distribution on the edges of the graph.

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Bottom-up broadcast neural network for music genre classification

Sheng-lan Liu, Lin Feng, Caifeng Liu, Guochao Liu, Huibing Wang•Wed Jan 23 2019

This paper develops a novel CNN architecture that takes the multi-scale time-frequency information into considerations, which transfers more suitable semantic features for the decision-making layer to discriminate the genre of the unknown music clip.

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Machine learning and chord based feature engineering for genre prediction in popular Brazilian music

Bruna D. Wundervald, Walmes M. Zeviani•Thu Feb 07 2019

It is found that features extracted from harmonic elements can satisfactorily predict music genre for the Brazilian case, as well as features obtained from the Spotify API, also known as the random forest model.

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Client-driven animated GIF generation framework using an acoustic feature

G. Mujtaba, Eun‐Seok Ryu, Sangsoon Lee, Jaehyoun Kim•Thu Feb 11 2021

This paper proposes a novel, lightweight method to generate animated graphical interchange format images (GIFs) using the computational resources of a client device that analyzes an acoustic feature from the climax section of an audio file to estimate the timestamp corresponding to the maximum pitch.

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Wasserstein Barycenter for Multi-Source Domain Adaptation

Eduardo Fernandes Montesuma, Fred Ngolè Mboula•Mon May 31 2021

Multi-source domain adaptation is a key technique that allows a model to be trained on data coming from various probability distribution. To overcome the challenges posed by this learning scenario, we propose a method for constructing an intermediate domain between sources and target domain, the Wasserstein Barycenter Transport (WBT). This method relies on the barycenter on Wasserstein spaces for aggregating the source probability distributions. Once the sources have been aggregated, they are transported to the target domain using standard Optimal Transport for Domain Adaptation framework. Additionally, we revisit previous single-source domain adaptation tasks in the context of multi-source scenario. In particular, we apply our algorithm to object and face recognition datasets. Moreover, to diversify the range of applications, we also examine the tasks of music genre recognition and music-speech discrimination. The experiments show that our method has similar performance with the existing state-of-the-art.

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