Modeling musicological information as trigrams in a system for simultaneous chord and local key extraction

In this paper, we discuss the introduction of a trigram musicological model in a simultaneous chord and local key extraction system. By enlarging the context of the musicological model, we hoped to achieve a higher accuracy that could justify the associated higher complexity and computational load of the search for the optimal solution. Experiments on multiple data sets have demonstrated that the trigram model has indeed a larger predictive power (a lower perplexity). This raised predictive power resulted in an improvement in the key extraction capabilities, but no improvement in chord extraction when compared to a system with a bigram musicological model

Estimation of the Reliability of Multiple Rhythm Features Extraction from a Single Descriptor.

ABSTRACT The provision of a reliability or confidence measure can be critical for the usage of a given feature in complex systems and real-world applications. However, feature extraction systems often do not provide one. In the present study we investigate the relationship between the entropy of a rhythmogram and the reliability of the extraction of multiple high level rhythm related features. The results show that this single descriptor has potential for estimating the reliability of multiple rhythm features extraction

Music-Theoretic Estimation of Chords and Keys from Audio

This paper proposes a new method for local key and chord estimation from audio signals. This method relies primarily on principles from music theory, and does not require any training on a corpus of labelled audio files. A harmonic content of the musical piece is first extracted by computing a set of chroma vectors. A set of chord/key pairs is selected for every frame by correlation with fixed chord and key templates. An acyclic harmonic graph is constructed with these pairs as vertices, using a musical distance to weigh its edges. Finally, the sequences of chords and keys are obtained by finding the best path in the graph using dynamic programming. The proposed method allows a mutual chord and key estimation. It is evaluated on a corpus composed of Beatles songs for both the local key estimation and chord recognition tasks, as well as a larger corpus composed of songs taken from the Billboard dataset

Стохастичні методи і методи машинного навчання в розпізнаванні музичних акордів

Дипломна робота: 143ст., 52 рис., 14 табл., 2 дод., та 21 джерело. Тема: Стохастичні методи і методи машинного навчання в розпізнаванні музичних акордів. У роботі розглянуто задачу розпізнавання музичних акордів за допомогою Марківських ланцюгів та методів машинного навчання. Об’єкт дослідження: система розпізнавання музичних акордів. Предмет дослідження: методи розпізнавання музичних акордів та їх порівняльний аналіз. Мета роботи: запропонувати та реалізувати модифікації існуючих методів розпізнавання з метою збільшення ступенів точності роботи даних методів. Методи дослідження: статистичні методи аналізу даних; методи машинного навчання. Створено програмний продукт для розпізнавання музичних акордів. Для проведення аналізу було використано розмічений набір даних Isophonics.Thesis: 143 pages, 52 figures, 14 tables, 2 appendices, and 21 sources. Topic: Stochastic methods and machine learning methods in the recognition of musical chords. The problem of recognition of musical chords by means of Markov chains and methods of machine learning is considered in the work. Object of research: system of recognition of musical chords. Subject of research: methods of music chord recognition and their comparative analysis. Purpose: to propose and implement modifications of existing recognition methods in order to increase the accuracy of these methods. Research methods: statistical methods of data analysis; machine learning methods. A software product for recognizing musical chords has been created. A labeled Isophonics data set was used for analysis

Probabilistic models for melodic prediction

AbstractChord progressions are the building blocks from which tonal music is constructed. The choice of a particular representation for chords has a strong impact on statistical modeling of the dependence between chord symbols and the actual sequences of notes in polyphonic music. Melodic prediction is used in this paper as a benchmark task to evaluate the quality of four chord representations using two probabilistic model architectures derived from Input/Output Hidden Markov Models (IOHMMs). Likelihoods and conditional and unconditional prediction error rates are used as complementary measures of the quality of each of the proposed chord representations. We observe empirically that different chord representations are optimal depending on the chosen evaluation metric. Also, representing chords only by their roots appears to be a good compromise in most of the reported experiments