Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

Towards efficient music genre classification using FastMap

Automatic genre classification aims to correctly categorize an unknown recording with a music genre. Recent studies use the Kullback-Leibler (KL) divergence to estimate music similarity then perform classification using k-nearest neighbours (k-NN). However, this approach is not practical for large databases. We propose an efficient genre classifier that addresses the scalability problem. It uses a combination of modified FastMap algorithm and KL divergence to return the nearest neighbours then use 1- NN for classification. Our experiments showed that high accuracies are obtained while performing classification in less than 1/20 second per track

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

Sequential Complexity as a Descriptor for Musical Similarity

We propose string compressibility as a descriptor of temporal structure in audio, for the purpose of determining musical similarity. Our descriptors are based on computing track-wise compression rates of quantised audio features, using multiple temporal resolutions and quantisation granularities. To verify that our descriptors capture musically relevant information, we incorporate our descriptors into similarity rating prediction and song year prediction tasks. We base our evaluation on a dataset of 15500 track excerpts of Western popular music, for which we obtain 7800 web-sourced pairwise similarity ratings. To assess the agreement among similarity ratings, we perform an evaluation under controlled conditions, obtaining a rank correlation of 0.33 between intersected sets of ratings. Combined with bag-of-features descriptors, we obtain performance gains of 31.1% and 10.9% for similarity rating prediction and song year prediction. For both tasks, analysis of selected descriptors reveals that representing features at multiple time scales benefits prediction accuracy.Comment: 13 pages, 9 figures, 8 tables. Accepted versio

The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use

The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our recent work, however, shows GTZAN has several faults (repetitions, mislabelings, and distortions), which challenge the interpretability of any result derived using it. In this article, we disprove the claims that all MGR systems are affected in the same ways by these faults, and that the performances of MGR systems in GTZAN are still meaningfully comparable since they all face the same faults. We identify and analyze the contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has been used in MGR research, and find few indications that its faults have been known and considered. Finally, we rigorously study the effects of its faults on evaluating five different MGR systems. The lesson is not to banish GTZAN, but to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference