Music Similarity Estimation

Music is a complicated form of communication, where creators and culture communicate and expose their individuality. After music digitalization took place, recommendation systems and other online services have become indispensable in the field of Music Information Retrieval (MIR). To build these systems and recommend the right choice of song to the user, classification of songs is required. In this paper, we propose an approach for finding similarity between music based on mid-level attributes like pitch, midi value corresponding to pitch, interval, contour and duration and applying text based classification techniques. Our system predicts jazz, metal and ragtime for western music. The experiment to predict the genre of music is conducted based on 450 music files and maximum accuracy achieved is 95.8% across different n-grams. We have also analyzed the Indian classical Carnatic music and are classifying them based on its raga. Our system predicts Sankarabharam, Mohanam and Sindhubhairavi ragas. The experiment to predict the raga of the song is conducted based on 95 music files and the maximum accuracy achieved is 90.3% across different n-grams. Performance evaluation is done by using the accuracy score of scikit-learn

Music genre visualization and classification exploiting a small set of high-level semantic features

In this paper a system for continuous analysis, visualization and classification of musical streams is proposed. The system performs visualization and classification task by means of three high-level, semantic features extracted computing a reduction on a multidimensional low-level feature vector through the usage of Gaussian Mixture Models. The visualization of the semantic characteristics of the audio stream has been implemented by mapping the value of the high-level features on a triangular plot and by assigning to each feature a primary color. In this manner, besides having the representation of musical evolution of the signal, we have also obtained representative colors for each musical part of the analyzed streams. The classification exploits a set of one-against-one threedimensional Support Vector Machines trained on some target genres. The obtained results on visualization and classification tasks are very encouraging: our tests on heterogeneous genre streams have shown the validity of proposed approac

Features for the classification and clustering of music in symbolic format

Tese de mestrado, Engenharia Informática, Universidade de Lisboa, Faculdade de Ciências, 2008Este documento descreve o trabalho realizado no âmbito da disciplina de Projecto em Engenharia Informática do Mestrado em Engenharia Informática da Faculdade de Ciências da Universidade de Lisboa. Recuperação de Informação Musical é, hoje em dia, um ramo altamente activo de investigação e desenvolvimento na área de ciência da computação, e incide em diversos tópicos, incluindo a classificação musical por géneros. O trabalho apresentado centra-se na Classificação de Pistas e de Géneros de música armazenada usando o formato MIDI. Para resolver o problema da classificação de pistas MIDI, extraimos um conjunto de descritores que são usados para treinar um classificador implementado através de uma técnica de Máquinas de Aprendizagem, Redes Neuronais, com base nas notas, e durações destas, que descrevem cada faixa. As faixas são classificadas em seis categorias: Melody (Melodia), Harmony (Harmonia), Bass (Baixo) e Drums (Bateria). Para caracterizar o conteúdo musical de cada faixa, um vector de descritores numérico, normalmente conhecido como ”shallow structure description”, é extraído. Em seguida, eles são utilizados no classificador — Neural Network — que foi implementado no ambiente Matlab. Na Classificação por Géneros, duas propostas foram usadas: Modelação de Linguagem, na qual uma matriz de transição de probabilidades é criada para cada tipo de pista midi (Melodia, Harmonia, Baixo e Bateria) e também para cada género; e Redes Neuronais, em que um vector de descritores numéricos é extraído de cada pista, e é processado num Classificador baseado numa Rede Neuronal. Seis Colectâneas de Musica no formato Midi, de seis géneros diferentes, Blues, Country, Jazz, Metal, Punk e Rock, foram formadas para efectuar as experiências. Estes géneros foram escolhidos por partilharem os mesmos instrumentos, na sua maioria, como por exemplo, baixo, bateria, piano ou guitarra. Estes géneros também partilham algumas características entre si, para que a classificação não seja trivial, e para que a robustez dos classificadores seja testada. As experiências de Classificação de Pistas Midi, nas quais foram testados, numa primeira abordagem, todos os descritores, e numa segunda abordagem, os melhores descritores, mostrando que o uso de todos os descritores é uma abordagem errada, uma vez que existem descritores que confundem o classificador. Provou-se que a melhor maneira, neste contexto, de se classificar estas faixas MIDI é utilizar descritores cuidadosamente seleccionados. As experiências de Classificação por Géneros, mostraram que os Classificadores por Instrumentos (Single-Instrument) obtiveram os melhores resultados. Quatro géneros, Jazz, Country, Metal e Punk, obtiveram resultados de classificação com sucesso acima dos 80% O trabalho futuro inclui: algoritmos genéticos para a selecção de melhores descritores; estruturar pistas e musicas; fundir todos os classificadores desenvolvidos num único classificador.This document describes the work carried out under the discipline of Computing Engineering Project of the Computer Engineering Master, Sciences Faculty of the Lisbon University. Music Information Retrieval is, nowadays, a highly active branch of research and development in the computer science field, and focuses several topics, including music genre classification. The work presented in this paper focus on Track and Genre Classification of music stored using MIDI format, To address the problem of MIDI track classification, we extract a set of descriptors that are used to train a classifier implemented by a Neural Network, based on the pitch levels and durations that describe each track. Tracks are classified into four classes: Melody, Harmony, Bass and Drums. In order to characterize the musical content from each track, a vector of numeric descriptors, normally known as shallow structure description, is extracted. Then they are used as inputs for the classifier which was implemented in the Matlab environment. In the Genre Classification task, two approaches are used: Language Modeling, in which a transition probabilities matrix is created for each type of track (Melody, Harmony, Bass and Drums) and also for each genre; and an approach based on Neural Networks, where a vector of numeric descriptors is extracted from each track (Melody, Harmony, Bass and Drums) and fed to a Neural Network Classifier. Six MIDI Music Corpora were assembled for the experiments, from six different genres, Blues, Country, Jazz, Metal, Punk and Rock. These genres were selected because all of them have the same base instruments, such as bass, drums, piano or guitar. Also, the genres chosen share some characteristics between them, so that the classification isn’t trivial, and tests the classifiers robustness. Track Classification experiments using all descriptors and best descriptors were made, showing that using all descriptors is a wrong approach, as there are descriptors which confuse the classifier. Using carefully selected descriptors proved to be the best way to classify these MIDI tracks. Genre Classification experiments showed that the Single-Instrument Classifiers achieved the best results. Four genres achieved higher than 80% success rates: Jazz, Country, Metal and Punk. Future work includes: genetic algorithms; structurize tracks and songs; merge all presented classifiers into one full Automatic Genre Classification System

Subject indexing of music

Sadržajna obrada glazbe ne vrši se samo od strane glazbenih profesionalaca među koje možemo uvrstiti glazbenike, producente, tonske majstore, glazbene urednike, glazbene knjižničare; tu su i neprofesionalci kojima pripadaju obični slušatelji, ali i među običnim slušateljima postoje poznavatelji određenog glazbenog žanra. Kada sadržajnu obradu glazbe vrše obični slušatelji tada to najčešće čine društvenim oznakama ili tagovima. Da bi se moglo razumjeti što su to društvene oznake rad će objasniti vrste i zadaće društvenih oznaka. Posebna pažnja će se usmjeriti na probleme koji se javljaju opisivanjem glazbe društvenim oznakama u mrežnim sustavima za reprodukciju, postavljanje i pretraživanje glazbenog sadržaja. Teorijski dio rada obuhvaća osnovnu teoriju glazbe, osobitosti primjene UDK u glazbenoj zbirci, načine označivanja glazbenih žanrova, definiciju društvenih oznaka, te kako ih koristiti kao preporuke. Nakon toga slijedi pregled istraživačkog dijela rada. Provedeno je kvantitativno istraživanje na slučajnom uzorku putem dva Google Forms upitnika. Istraživanju je pristupilo 80 ispitanika, od čega 43 ispitanika u prvom upitniku, dok u drugom upitniku njih 37. U prvom upitniku htjelo se istražiti na koji način će ispitanici opisati ponuđene pjesme pomoću predloženih oznaka, dok u drugom upitniku, iste pjesme samo bez predloženih oznaka. Nadalje, htjelo se istražiti da li će ispitanici najviše koristiti kategoriju žanr u oba upitnika. Rezultati su pokazali da ispitanici ukoliko im se predlože oznake za opis pjesama najviše koriste kategoriju žanr, a kada sami dodjeljuju oznake pjesmama tada više koriste kategoriju raspoloženje.The subject indexing of music is not only performed by professionals in the music industry, among which we can include musicians, producers, sound engineers, music editors, and music librarians; there are also non-professionals which include ordinary listeners and among them those that are especially familiar with a particular musical genre. When subject indexing of music is done by ordinary listeners, it is usually done by social tags or labels through the process commonly known as collaborative tagging. In order to be able to understand what these social tags mean, this work will explain the types and tasks of social tags. Special attention will be given to problems that arise when describing music with social tags in network systems for playback, setup, and when searching for musical content. The theoretical part of this paper encompasses the basic theory of music, the peculiarities of applying the Universal Decimal Classification for music collections, ways of marking musical genres, the definition of social tags and how to use them as recommendations. This is followed by the research part of the work. A survey was conducted through two Google Forms surveys. 80 respondents took part in the surveys, of which 43 respondents took part in the first survey, and 37 of them took part in the second survey. The first survey was used to explore how respondents describe the offered songs using the suggested tags, while the second survey used the same song but without the proposed tags. Furthermore, it was investigated whether the respondents would mostly use the genre category in both surveys. The results showed that respondents, when given description tags of songs would use the genre category more often, and when they would assign the tags themselves, they would choose the mood category

Evaluation of content-based acoustic features for musical genre classification

In this thesis, we evaluate content-based acoustic features for musical genre classification. Effectiveness of various acoustic features are compared using a k-nearest neighbor (KNN) classifier. By utilizing the combinations of acoustic features, an average classification accuracy of $89\%$ for GTZAN database is achieved, which is comparable to prior work. A statistical test, McNemar's test, is applied to support the idea that musical genre is intrinsically related to content-based acoustic features. Especially for some genres, we are able to identify the particular associated acoustic property. In addition, by comparing our KNN results to a psychoacoustic listening experiment, we associate various human perceptual dimensions with low-level acoustic features

Algorithmic composer, an unconventional music classification system

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsMusic is an inherent part of the human existence. As an art, it has mirrored its evolution and captured its thinking and creative process over the years. Given its importance and complexity, machine learning has long embraced the challenge of analyzing music, mainly through recommendation systems, classification and composition tasks. Current classification systems work on the base of feature extraction and analysis. The same applies for music classification algorithms, which require the formulation of characteristics of the songs. Such characteristics can be of varying degrees of complexity, from spectrogram analysis to simpler rhythmic and melodic features. However, finding characteristics to faithfully describe music is not only conceptually hard, but mainly too simplistic and restrictive given its complex nature. A new methodology for music classification systems is proposed in this thesis, which aims to show that the knowledge learned by state of the art composition systems can be used for classification, without need for direct feature extraction. Using an architecture of recurrent neural networks (RNN) and long-short term memory cells (LSTMs) for the composition systems and implementing a voting scheme between them, the classification accuracy of the experiments between classes of the Nottingham dataset ranged between 60% and 95%. These results provide strong evidence that composition systems do indeed possess valuable information to distinguish between classes of music. They also prove that an alternative method to standard classification is possible, as classification targets are not directly used for training. Finally, the extent to which these results can be used for other applications is discussed, namely its added value to more complex classification systems, as well as to recommendation systems.A Música é uma componente inerente à existência humana. Enquanto arte, tem refletido a sua evolução e captado o seu processo cognitivo e criativo ao longo dos tempos. Tendo em conta a sua importância e complexidade, a área do Machine Learning desde há muito abraçou este desafio, sobretudo através de sistemas de recomendação, classificação e composição musical. Os sistemas de recomendação atuais funcionam na base de extração de features e respetiva análise. O mesmo se aplica a algoritmos de classificação musical, que requerem a formulação de características musicais. Estas podem ter diferentes graus de complexidade, desde análise de espectros a simples features melódicas e rítmicas. Contudo, formular caracteríticas musicais não só é conceptualmente difícil, como sobretudo simplista e restritivo dada a sua natureza complexa. Uma nova metodologia para sistemas de classificação musical é proposta nesta tese, com o objectivo de demonstrar que o conhecimento aprendido por sistemas de composição pode ser utilizado para classificação, sem que haja necessidade de um processo de conceptualização e extração de características. Utilizando uma arquitectura de redes neuronais recorrentes e células de memória longa e curta para os sistemas de composição e implementando um sistema de votação entre eles, a precisão para classificações binárias entre as classes do Nottingham dataset variou entre 60% e 95%. Estes resultados demonstram uma forte evidência de que os algoritmos de composição podem ser utilizados para tarefas de classificação e provam ainda que um método alternativo à classificação convencional é possível. Finalmente, a aplicabilidade destes resultados para outros projetos é discutida, nomeadamente o valor acrescentado que pode trazer para sistemas de classificação mais complexos, assim como a sistemas de recomendação