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

Recognition of variations using automatic Schenkerian reduction.

Experiments on techniques to automatically recognise whether or not an extract of music is a variation of a given theme are reported, using a test corpus derived from ten of Mozart's sets of variations for piano. Methods which examine the notes of the 'surface' are compared with methods which make use of an automatically derived quasi-Schenkerian reduction of the theme and the extract in question. The maximum average F-measure achieved was 0.87. Unexpectedly, this was for a method of matching based on the surface alone, and in general the results for matches based on the surface were marginally better than those based on reduction, though the small number of possible test queries means that this result cannot be regarded as conclusive. Other inferences on which factors seem to be important in recognising variations are discussed. Possibilities for improved recognition of matching using reduction are outlined

Comparison Of Modified Dual Ternary Indexing And Multi-Key Hashing Algorithms For Music Information Retrieval

In this work we have compared two indexing algorithms that have been used to index and retrieve Carnatic music songs. We have compared a modified algorithm of the Dual ternary indexing algorithm for music indexing and retrieval with the multi-key hashing indexing algorithm proposed by us. The modification in the dual ternary algorithm was essential to handle variable length query phrase and to accommodate features specific to Carnatic music. The dual ternary indexing algorithm is adapted for Carnatic music by segmenting using the segmentation technique for Carnatic music. The dual ternary algorithm is compared with the multi-key hashing algorithm designed by us for indexing and retrieval in which features like MFCC, spectral flux, melody string and spectral centroid are used as features for indexing data into a hash table. The way in which collision resolution was handled by this hash table is different than the normal hash table approaches. It was observed that multi-key hashing based retrieval had a lesser time complexity than dual-ternary based indexing The algorithms were also compared for their precision and recall in which multi-key hashing had a better recall than modified dual ternary indexing for the sample data considered.Comment: 11 pages, 5 figure

The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation

With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity. Despite very promising progress on image and short sequence generation, symbolic music generation remains a challenging problem since the structure of compositions are usually complicated. In this study, we attempt to solve the melody generation problem constrained by the given chord progression. This music meta-creation problem can also be incorporated into a plan recognition system with user inputs and predictive structural outputs. In particular, we explore the effect of explicit architectural encoding of musical structure via comparing two sequential generative models: LSTM (a type of RNN) and WaveNet (dilated temporal-CNN). As far as we know, this is the first study of applying WaveNet to symbolic music generation, as well as the first systematic comparison between temporal-CNN and RNN for music generation. We conduct a survey for evaluation in our generations and implemented Variable Markov Oracle in music pattern discovery. Experimental results show that to encode structure more explicitly using a stack of dilated convolution layers improved the performance significantly, and a global encoding of underlying chord progression into the generation procedure gains even more.Comment: 8 pages, 13 figure

The Semantic Web MIDI Tape: An Interface for Interlinking MIDI and Context Metadata

The Linked Data paradigm has been used to publish a large number of musical datasets and ontologies on the Semantic Web, such as MusicBrainz, AcousticBrainz, and the Music Ontology. Recently, the MIDI Linked Data Cloud has been added to these datasets, representing more than 300,000 pieces in MIDI format as Linked Data, opening up the possibility for linking fine-grained symbolic music representations to existing music metadata databases. Despite the dataset making MIDI resources available in Web data standard formats such as RDF and SPARQL, the important issue of finding meaningful links between these MIDI resources and relevant contextual metadata in other datasets remains. A fundamental barrier for the provision and generation of such links is the difficulty that users have at adding new MIDI performance data and metadata to the platform. In this paper, we propose the Semantic Web MIDI Tape, a set of tools and associated interface for interacting with the MIDI Linked Data Cloud by enabling users to record, enrich, and retrieve MIDI performance data and related metadata in native Web data standards. The goal of such interactions is to find meaningful links between published MIDI resources and their relevant contextual metadata. We evaluate the Semantic Web MIDI Tape in various use cases involving user-contributed content, MIDI similarity querying, and entity recognition methods, and discuss their potential for finding links between MIDI resources and metadata