Main melody extraction for polyphonic music

[[abstract]]We propose an approach to main melody extraction from multi-track polyphonic MIDI files. In each track of a MIDI file, the system traces the pitch contour of the polyphonic music, and describes it in a monophonic form. In each of these contours, maximal repeating patterns are found using a correlative matrix. All of these patterns are then collected in a dictionary, with which we can find the set of all maximal repeating patterns with no redundancy called the main melody. The main melody extraction results and how well these extracted main melodies can improve the work on content-based music retrieval are given and described.[[sponsorship]]教育部; 國家科學委員會; 東華大學[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20021218~20021221[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Hualien, Taiwan[[conferencelocation]]花蓮縣, 臺

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

JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs

We propose a novel approach for the generation of polyphonic music based on LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord progression based on a chord embedding. A second LSTM then generates polyphonic music from the predicted chord progression. The generated music sounds pleasing and harmonic, with only few dissonant notes. It has clear long-term structure that is similar to what a musician would play during a jam session. We show that our approach is sensible from a music theory perspective by evaluating the learned chord embeddings. Surprisingly, our simple model managed to extract the circle of fifths, an important tool in music theory, from the dataset.Comment: Paper presented at the 29th International Conference on Tools with Artificial Intelligence, ICTAI 2017, Boston, MA, US

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

Content Based Music Retrieval Dengan Teknik Approximate String Matching

ABSTRAKSI: Pencarian musik saat ini kebanyakan masih sebatas pada query berupa judul musik atau nama penyanyinya. Pencarian musik berdasarkan content masih jarang, misalkan pencarian musik berdasarkan melodi. Namun bagimana caranya mencocokkan 2 berkas musik dengan susunan nada yang berbeda sehingga bisa dikatakan mempunyai melodi yang sama.Tugas akhir ini membangun sebuah aplikasi yang dapat melakukan pencarian musik berdasarkan query berupa potongan melodi ataupun full melodi menggunakan teknik Approximate String Matching, dengan algoritma untuk proses pencocokkan adalah Levenshtein Distance. Approximate String Matching merupakan teknik pencocokkan string yang memperbolehkan adanya eror. Representasi digital yang akan digunakan dalam bentuk file MIDI.Langkah-langkah yang digunakan pada proses pencarian musik terdiri dari 3, yaitu ekstraksi melodi, standarisasi melodi, dan pencocokkan melodi. Pada saat user menginputkan query, sistem akan membaca file MIDI dan mengubah informasi midi ke dalam bentuk teks, setelah itu barulah ke tiga proses tadi dilakukan.Aplikasi ini dapat melakukan pencarian musik yang menghasilkan output berupa daftar musik-musik yang di dalamnya terdapat unsur melodi yang terdapat pada input query. Jika dibandingkan dengan metode Exact Matching, maka metode ini lebih bisa digunakan untuk proses query musik. Pada metode ini, panjang input query berbanding lurus dengan waktu yang dibutuhkan untuk pemrosesan query.Kata Kunci : query musik, content musik, approximate string matching,ABSTRACT: Nowadays, most music query is based on its title or singer. Content based music query such as melody based query is quite unpopular. However, the problem is how to match two music files with different sequence of notes, so they can be concluded as having the same melody.In the final project, a software can be used to do music query based on either partial or complete melody by Approximate string matching method, using Levenshtein Distance algorithm is developed. Approximate string matching is a string matching algorithm which allows an error. Each music is represented in digital form as MIDI files.To perform the music query, three steps are required. These steps are melody extraction, melody standardization, and melody matching. As user input the query, system will read MIDI files convert the information they contain into text. Afterwards these three steps mentioned above are executed sequentially.This software is able to do music query that generate a list of music file which contain inputed query. Compared to Exact matching, this method is more appropriate to do music query. In this method, query length is equivalent to the time required for query execution.Keyword: music query, music content, approximate string matching, levensthei

Methodological considerations concerning manual annotation of musical audio in function of algorithm development

In research on musical audio-mining, annotated music databases are needed which allow the development of computational tools that extract from the musical audiostream the kind of high-level content that users can deal with in Music Information Retrieval (MIR) contexts. The notion of musical content, and therefore the notion of annotation, is ill-defined, however, both in the syntactic and semantic sense. As a consequence, annotation has been approached from a variety of perspectives (but mainly linguistic-symbolic oriented), and a general methodology is lacking. This paper is a step towards the definition of a general framework for manual annotation of musical audio in function of a computational approach to musical audio-mining that is based on algorithms that learn from annotated data. 1

A scheme for content based retrieval of music data in MIDI format

With phenomenal increases in the generation and storage of digital audio data in several applications, there is growing need for organizing audio data in databases and providing users with fast access to desired data. This paper presents a scheme for the content-based query and retrieval of audio data stored in MIDI format. This is based on extraction of melody from the MIDI files and suitably comparing with the melody of the query. The results of retrieval using the proposed algorithm are presented.<br /