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Multimodal Deep Learning for Music Genre Classification

Sergio Oramas, Francesco Barbieri, Oriol Nieto, Xavier Serra
2018 Transactions of the International Society for Music Information Retrieval  
In this work, an approach to learn and combine multimodal data representations for music genre classification is proposed.  ...  Intermediate representations of deep neural networks are learned from audio tracks, text reviews, and cover art images, and further combined for classification.  ...  Music genres have been widely used for music classification, from physical music stores to streaming services.  ... 
doi:10.5334/tismir.10 fatcat:xfkr3e3atne3hbiwoyaxqv35za

Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features [article]

Sergio Oramas, Oriol Nieto, Francesco Barbieri, Xavier Serra
2017 arXiv   pre-print
Additionally, we propose an approach for multi-label genre classification based on the combination of feature embeddings learned with state-of-the-art deep learning methodologies.  ...  Music genres allow to categorize musical items that share common characteristics.  ...  The Tesla K40 used for this research was donated by the NVIDIA Corporation.  ... 
arXiv:1707.04916v1 fatcat:pfpjy6vdyvazndl2tvste5ms5i

A Multimodal Convolutional Neural Network Model for the Analysis of Music Genre on Children's Emotions Influence Intelligence

Wei Chen, Guobin Wu, Ning Cao
2022 Computational Intelligence and Neuroscience  
This paper designs a multimodal convolutional neural network model for the intelligent analysis of the influence of music genres on children's emotions by constructing a multimodal convolutional neural  ...  network model and profoundly analyzing the impact of music genres on children's feelings.  ...  Genres on Children's Emotions e most significant difficulty in feature selection for music classification can be solved by deep learning models, which allow computers to automatically learn the pattern  ... 
doi:10.1155/2022/5611456 pmid:36072733 pmcid:PMC9444378 fatcat:5573ddgjwbhw3oua4wjdnvhli4

Multi-Label Music Genre Classification From Audio, Text And Images Using Deep Features

Sergio Oramas, Oriol Nieto, Francesco Barbieri, Xavier Serra
2017 Zenodo  
However, the authors are not aware of published multimodal approaches for music genre classification that involve deep learning. Multi-label classification is a widely studied problem [14, 43] .  ...  To this end, we present MuMu, a new large-scale multimodal dataset for multi-label music genre classification.  ... 
doi:10.5281/zenodo.1417427 fatcat:2bd3ge36zncixdez4hgogwsfdy

Deep learning for video game genre classification [article]

Yuhang Jiang, Lukun Zheng
2020 arXiv   pre-print
In this paper, we propose a multi-modal deep learning framework to solve this problem. The contribution of this paper is four-fold.  ...  Second, image-based and text-based, state-of-the-art models are evaluated thoroughly for the task of genre classification for video games.  ...  Finally, we consider a multimodal deep learning architecture based on both the game cover and description for the task of genre classification.  ... 
arXiv:2011.12143v1 fatcat:74rkilllk5hedipe5ehwdn25qu

The Classification of Music and Art Genres under the Visual Threshold of Deep Learning

Zhiqiang Zheng, Vijay Kumar
2022 Computational Intelligence and Neuroscience  
This paper aims to develop a novel deep learning-enabled music genre classification (DLE-MGC) technique.  ...  However, music is a kind of time-series data, and it is challenging to design an effective music genre classification (MGC) system due to a large quantity of music data.  ...  (i) is paper focuses on developing a novel deep learning-enabled music genre classification (DLE-MGC) technique.  ... 
doi:10.1155/2022/4439738 pmid:35634048 pmcid:PMC9132639 fatcat:m7sz3sj3tfbbnm6dro2qfbmque

A Novel Multimodal Music Genre Classifier using Hierarchical Attention and Convolutional Neural Network [article]

Manish Agrawal, Abhilash Nandy
2020 arXiv   pre-print
Music genre classification is one of the trending topics in regards to the current Music Information Retrieval (MIR) Research.  ...  We implemented a CNN based feature extractor for spectrograms in order to incorporate the acoustic features and a Hierarchical Attention Network based feature extractor for lyrics.  ...  Introduction Genre classification of music tracks is a well known researched task in the area of music research community.  ... 
arXiv:2011.11970v1 fatcat:xmaebuslz5fp3cflqbhjaalxde

Design of Neural Network Model for Cross-Media Audio and Video Score Recognition Based on Convolutional Neural Network Model

Hongxia Liu, Gengxin Sun
2022 Computational Intelligence and Neuroscience  
generate music of different genres.  ...  The accuracy of the experiment is illustrated by comparing the spectrogram and the spectrogram, and genre classification predictions are also performed on the generated music to show that the network can  ...  music predictions for each genre are generated, and then the music is used for genre prediction.  ... 
doi:10.1155/2022/4626867 pmid:35733575 pmcid:PMC9208963 fatcat:xowneyawdjdxdg5maxydthilji

Knowledge Extraction And Representation Learning For Music Recommendation And Classification

Sergio Oramas, Xavier Serra
2017 Zenodo  
Next, we focus on learning new data representations from multimodal content using deep learning architectures, addressing the problems of cold-start music recommendation and multi-label music genre classification  ...  Then, we show how modeling semantic information may impact musicological studies and helps to outperform purely text-based approaches in music similarity, classification, and recommendation.  ...  Conclusions An approach for multi-label music genre classification using deep learning architectures has been proposed.  ... 
doi:10.5281/zenodo.1100973 fatcat:yfpmc6qxbbakjp6qzvywyoaoci

Knowledge Extraction And Representation Learning For Music Recommendation And Classification

Sergio Oramas, Xavier Serra
2017 Zenodo  
Next, we focus on learning new data representations from multimodal content using deep learning architectures, addressing the problems of cold-start music recommendation and multi-label music genre classification  ...  Then, we show how modeling semantic information may impact musicological studies and helps to outperform purely text-based approaches in music similarity, classification, and recommendation.  ...  Conclusions An approach for multi-label music genre classification using deep learning architectures has been proposed.  ... 
doi:10.5281/zenodo.1048497 fatcat:kdh5jhvocbh3riwln6n2f756su

Multimodal Music Emotion Recognition Method Based on the Combination of Knowledge Distillation and Transfer Learning

Guiying Tong, Baiyuan Ding
2022 Scientific Programming  
This paper proposes a multimodal method based on the combination of knowledge distillation and music style transfer learning and verifies the effectiveness of the method on 20,000 songs.  ...  Not only is accurate labeling of emotion categories costly and time-consuming, but it also requires extensive musical background for labelers At the same time, the emotion of music is often affected by  ...  Some scholars have achieved excellent results in the music emotion classification competition task through the indepth application of deep learning technology (Music Information Retrieval Evaluation Exchange  ... 
doi:10.1155/2022/2802573 fatcat:l2k2cern7rdi7gn55vsa25mriy

Gated Multimodal Units for Information Fusion [article]

John Arevalo, Thamar Solorio, Manuel Montes-y-Gómez, Fabio A. González
2017 arXiv   pre-print
It was evaluated on a multilabel scenario for genre classification of movies using the plot and the poster.  ...  This paper presents a novel model for multimodal learning based on gated neural networks.  ...  The authors also thank for K40 Tesla GPU donated by NVIDIA and which was used for some representation learning experiments.  ... 
arXiv:1702.01992v1 fatcat:pvia2tc3gvbanhdz7bs3j6ws3y

A Multi-Modal Convolutional Neural Network Model for Intelligent Analysis of the Influence of Music Genres on Children's Emotions

Qingfang Qian, Xiaofeng Chen, Qiangyi Li
2022 Computational Intelligence and Neuroscience  
Therefore, for the task of the influence of music genres on children's emotions, this paper proposes a transformer-based multi-modal convolutional neural network.  ...  The influence of music genres on children's emotional intelligence is one of the hot topics in the field of multi-modal emotion research.  ...  To solve these problems, current deep learning-based methods mainly classify different types of music genres by rhythm, melody, harmony, sound intensity, and granularity.  ... 
doi:10.1155/2022/4957085 pmid:35909819 pmcid:PMC9325589 fatcat:i7daghjcgfd5fogu56xfc2dlly

A multimodal approach for multi-label movie genre classification [article]

Rafael B. Mangolin, Rodolfo M. Pereira, Alceu S. Britto Jr., Carlos N. Silla Jr., Valéria D. Feltrim, Diego Bertolini, Yandre M. G. Costa
2020 arXiv   pre-print
In this paper, we addressed the multi-label classification of the movie genres in a multimodal way.  ...  Movie genre classification is a challenging task that has increasingly attracted the attention of researchers.  ...  the Improvement of Higher Education Personnel and FA -Araucária Foundation for their financial support for their financial support.  ... 
arXiv:2006.00654v1 fatcat:kzavogn6mrd6vfpthukqywa6bi

Multimodal Metric Learning for Tag-based Music Retrieval [article]

Minz Won, Sergio Oramas, Oriol Nieto, Fabien Gouyon, Xavier Serra
2020 arXiv   pre-print
In this paper, we investigate three ideas to successfully introduce multimodal metric learning for tag-based music retrieval: elaborate triplet sampling, acoustic and cultural music information, and domain-specific  ...  Also, metric learning has already proven its suitability for cross-modal retrieval tasks in other domains (e.g., text-to-image) by jointly learning a multimodal embedding space.  ...  Recent work in MIR showed the advantage of using metric learning with pretrained word embeddings for audio-based music tagging and classification [9] .  ... 
arXiv:2010.16030v1 fatcat:opjfd2xoc5avnhjgkmlf7e3f3u
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