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Automatic Outlier Detection In Music Genre Datasets

Yen-Cheng Lu, Chih-Wei Wu, Alexander Lerch, Chang-Tien Lu
2016 Zenodo  
The results show that all of the methods fail to identify the outliers with reasonably high accuracy. This leaves room for future improvement in the automatic detection of outliers in music data.  ...  CONCLUSION In this paper, we have presented the application of outlier detection methods on a music dataset.  ... 
doi:10.5281/zenodo.1418192 fatcat:2ca5zj27anei3kz3tvuy4wdcsq

An Unsupervised Approach to Anomaly Detection in Music Datasets

Yen-Cheng Lu, Chih-Wei Wu, Chang-Tien Lu, Alexander Lerch
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
When applied to a music genre recognition dataset, the new method is able to detect corrupted, distorted, or mislabeled audio samples based on commonly used features in music information retrieval.  ...  This paper presents an unsupervised method for systematically identifying anomalies in music datasets.  ...  CONCLUSION In this paper, we have presented an unsupervised approach for automatic anomaly detection in music datasets.  ... 
doi:10.1145/2911451.2914700 dblp:conf/sigir/LuWLL16 fatcat:dogpxumtoraopfv32dejpvmf6u

Learning A Feature Space For Similarity In World Music

Maria Panteli, Emmanouil Benetos, Simon Dixon
2016 Zenodo  
Outlier Detection The second experiment to evaluate the learned space aims at detecting outliers in the dataset.  ...  Here we have used a small dataset to assess similarity as estimated by classification and outlier detection tasks.  ... 
doi:10.5281/zenodo.1415216 fatcat:66rkyzqtmfda7pm23on5o7n5pu

A computational study on outliers in world music

Maria Panteli, Emmanouil Benetos, Simon Dixon, Chun-Hsi Huang
2017 PLoS ONE  
We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation  ...  In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as 'outliers'.  ...  [62] compare outlier detection techniques applied on a music genre recognition dataset. Hansen et al.  ... 
doi:10.1371/journal.pone.0189399 pmid:29253027 pmcid:PMC5734747 fatcat:7dhbkgtfy5fevj2tyu3goged24

Improving Generalization of Deep Learning Music Classifiers

Morgan Buisson, Pablo Alonso, Dmitry Bogdanov
2021 Zenodo  
We first propose ways to maximize the amount of information extracted from small datasets through outliers detection and eÿcient audio data augmentation.  ...  More specifically, we suggest a set of guidelines to be followed in order to address the generalization problem of music classifiers trained on very small datasets.  ...  We described the high complexity of automatically treating audio outliers, especially in the context of music classification.  ... 
doi:10.5281/zenodo.5554754 fatcat:thqdptf6qfcjtaz5txu5tmj6vq

Testing music selection automation possibilities for video ads

Oliver Wiesener
2017 Management şi Marketing  
Humans typically decide which music is to be used in a video ad.  ...  Since the digitization progress in the music sector is currently mainly focused on music composing this article strives for taking a first step towards the digitization of the music selection.  ...  Before applying the statistical tests, the dataset will be checked in regard to outliers and in regard to correlations between the genres. Boxplots will contribute to identify outliers.  ... 
doi:10.1515/mmcks-2017-0026 fatcat:w2hozc4sfvanzj3v7ie24ixsdu

Hierarchical Novelty Detection [chapter]

Paolo Simeone, Raúl Santos-Rodríguez, Matt McVicar, Jefrey Lijffijt, Tijl De Bie
2017 Lecture Notes in Computer Science  
In music genre classification for example, there are numerous music genres (multi-class) which are hierarchically related.  ...  Empirical results on a music genre classification problem are reported, comparing with a state-of-theart method as well as simple benchmarks.  ...  in the dataset.  ... 
doi:10.1007/978-3-319-68765-0_26 fatcat:uxifsqj2z5fpjnapotormsgwue

Multimodal Semantics Extraction from User-Generated Videos

Francesco Cricri, Kostadin Dabov, Mikko J. Roininen, Sujeet Mate, Igor D. D. Curcio, Moncef Gabbouj
2012 Advances in Multimedia  
Furthermore we propose a method that automatically identifies the optimal set of cameras to be used in a multicamera video production.  ...  In this work we develop methods that analyze contextual information of multiple user-generated videos in order to obtain semantic information about public happenings (e.g., sport and live music events)  ...  Dataset 1 contains data (user-generated videos and associated context data) collected at public events of both sport genre and live music genre, held either indoors or outdoors and in stadium or nonstadium  ... 
doi:10.1155/2012/292064 fatcat:szf4rdd2rva6bbmuflfhs3szdu

Automated music selection of video ads

Oliver Wiesener
2017 Proceedings of the International Conference on Business Excellence  
Since the digitization progress in the music sector is mainly focused on music composing this article strives for making a first step towards the digitization of the music selection.  ...  Often the visual sense is busy in the sense that users focus other screens than the screen with the video ad. This is called the second screen syndrome.  ...  Therefore, no data will be excluded from the dataset based on the outlier analysis.  ... 
doi:10.1515/picbe-2017-0077 fatcat:7bmf6gwugndbnlilwff4inqlru

GuitarSet: A Dataset for Guitar Transcription

Qingyang Xi, Rachel Bittner, Johan Pauwels, Xuzhou Ye, Juan Pablo Bello
2018 Zenodo  
In this paper we present GuitarSet, a dataset that provides high quality guitar recordings alongside rich annotations and metadata.  ...  The dataset contains recordings of a variety of musical excerpts played on an acoustic guitar, along with time-aligned annotations of string and fret positions, chords, beats, downbeats, and playing style  ...  The guitarists were asked to play 30 twelve to sixteen bar excerpts from lead-sheets in a variety of keys, tempos, and musical genres, described in Section 4.  ... 
doi:10.5281/zenodo.1492449 fatcat:nxbkqxieijaqxezbhxodb4kp7q

BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis [article]

Davide Moltisanti, Jinyi Wu, Bo Dai, Chen Change Loy
2022 arXiv   pre-print
Our dataset can readily foster advance in dance motion synthesis.  ...  These characteristics are found in all existing datasets for dance motion synthesis, and indeed recent methods can achieve good results.We introduce a new dataset aiming to challenge these common assumptions  ...  This study is supported under the RIE2020 Industry Alignment Fund -Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).  ... 
arXiv:2207.10120v2 fatcat:u2ozlg6u7nbmlimuuhj542amou

Automatic Music Genres Classification using Machine Learning

Muhammad Asim, Zain Ahmed
2017 International Journal of Advanced Computer Science and Applications  
Classification of music genre has been an inspiring job in the area of music information retrieval (MIR).  ...  Overall the Support Vector Machine (SVM) is much more effective classifier for classification of music genre. It gave an overall accuracy of 77%.  ...  Similarly, another paper automatic musical genre classification of audio signals [21] in which a vector of size 9 (Mean-Centroid, Mean-Rolloff, Mean-Flux, Mean-Zero-Crossings, std centroid, std Rolloff  ... 
doi:10.14569/ijacsa.2017.080844 fatcat:ugtqazoqxjfyvnibt766rn6rcy

Databionic Visualization Of Music Collections According To Perceptual Distance

Fabian Mörchen, Alfred Ultsch, Mario Nöcker, Christian Stamm
2005 Zenodo  
Visualization and clustering with U-Maps can help in detecting outliers and timbrally consistent groups of music in unlabeled datasets.  ...  Both global and local structures in music collections are successfully detected.  ... 
doi:10.5281/zenodo.1417966 fatcat:bjwojcmpmfcprf6m4xgr5ryuzm

Genre Classification Based On Predominant Melodic Pitch Contours

Bruno M Rocha, Emilia Gómez, Justin Salamon
2018 Zenodo  
We present an automatic genre classification system based on melodic features. First a ground truth genre dataset composed of polyphonic music excerpts is compiled.  ...  We compare different standard classification algorithms to automatically classify genre using the extracted features.  ...  Xavier Serra for giving me the opportunity to participate in this master. Perfecto Herrera and Enric Guaus for the suggestions and advices over this year. All my colleagues and members of the MTG for  ... 
doi:10.5281/zenodo.1162804 fatcat:f2f4zhifezadfgvqbaeqxkq2ju

Lyric-Based Classification of Music Genres Using Hand-Crafted Features

Curtis Thompson
2021 Reinvention: an International Journal of Undergraduate Research  
The classification of music genres has been studied using various auditory, linguistic, and metadata features.  ...  We use these features to train traditional machine learning models for lyrical classification across nine popular music genres and compare their performance.  ...  The range for Flesch reading ease, when excluding outliers, varies depending on the genre.  ... 
doi:10.31273/reinvention.v14i2.705 fatcat:i6zptml5ivaztghlyrz22nezzu
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