Matt McVicar

An End-to-End Machine Learning System for Harmonic Analysis of Music

We present a new system for the harmonic analysis of popular musical audio. It is focused on chord estimation, although the proposed system additionally estimates the key sequence and bass notes. It is distinct from competing approaches in two main ways. First, it makes use of a new improved chromagram representation of audio that takes the human perception of loudness into account. Furthermore, it is the first system for joint estimation of chords, keys, and bass notes that is fully based on machine learning, requiring no expert knowledge to tune the parameters. This means that it will benefit from future increases in available annotated audio files, broadening its applicability to a wider range of genres. In all of three evaluation scenarios, including a new one that allows evaluation on audio for which no complete ground truth annotation is available, the proposed system is shown to be faster, more memory efficient, and more accurate than the state-of-the-art.

Automatic Chord Estimation from Audio: A Review of the State of the Art

In this overview article, we review research on the task of Automatic Chord Estimation (ACE). The major contributions from the last 14 years of research are summarized, with detailed discussions of the following topics: feature extraction, modeling strategies, model training and datasets, and evaluation strategies. Results from the annual benchmarking evaluation Music Information Retrieval Evaluation eXchange (MIREX) are also discussed as well as developments in software implementations and the impact of ACE within MIR. We conclude with possible directions for future research.

Using Online Chord Databases to Enhance Chord Recognition

Abstract Advances in chord recognition research using machine learning are hampered by two factors: the scarcity of annotated training data, and the limited complexity of the features and models used. Both problems are intertwined, as with few training examples, increasing the complexity of the model would inevitably lead to overfitting. In this paper we develop a way to address the first problem by exploiting chord annotations from online chord databases. We show how such chord annotations, despite being noisy and lacking exact chord onset times, can be put to use both during the recognition and training stage. We note that the ability to exploit this large untapped resource may enable researchers to also address the second problem: with more training data, one may be able to use more complex models without running the same high risk of overfitting.

Enhancing chord recognition accuracy using web resources

Machine learning methods for chord recognition have improved considerably in the past few years. However, further progress seems constrained by the scarcity of training data. In this paper, we show that this problem can be partially solved by exploiting noisy but freely and abundantly available online resources, in addition to fully labeled training data. We use these data to restrict the output of the Viterbi algorithm, resulting in significant improvements over the standard decoding process.

Hierarchical Novelty Detection

Hierarchical classification is commonly defined as multi-class classification where the classes are hierarchically nested. Many practical hierarchical classification problems also share features with multi-label classification (i.e., each data point can have any number of labels, even non-hierarchically related) and novelty detection (i.e., some data points are novelties at some level of the hierarchy). A further complication is that it is common for training data to be incompletely labelled, e.g. the most specific labels are not always provided. In music genre classification for example, there are numerous music genres (multi-class) which are hierarchically related. Songs can belong to different (even non-nested) genres (multi-label), and a song labelled as Rock may not belong to any of its sub-genres, such that it is a novelty within this genre (novelty-detection). Finally, the training data may label a song as Rock whereas it really could be labelled correctly as the more specific genre Blues Rock. In this paper we develop a new method for hierarchical classification that naturally accommodates every one of these properties. To achieve this we develop a novel approach, modelling it as a Hierarchical Novelty Detection problem that can be trained through a single convex second-order cone programming problem. This contrasts with most existing approaches that typically require a model to be trained for each layer or internal node in the label hierarchy. Empirical results on a music genre classification problem are reported, comparing with a state-of-the-art method as well as simple benchmarks.

SuMoTED: An intuitive edit distance between rooted unordered uniquely-labelled trees

Defining and computing distances between tree structures is a classical area of study in theoretical computer science, with practical applications in the areas of computational biology, information retrieval, text analysis, and many others. In this paper, we focus on rooted, unordered, uniquely-labelled trees such as taxonomies and other hierarchies. For trees as these, we introduce the intuitive concept of a ‘local move’ operation as an atomic edit of a tree. We then introduce SuMoTED, a new edit distance measure between such trees, defined as the minimal number of local moves required to convert one tree into another. We show how SuMoTED can be computed using a scalable algorithm with quadratic time complexity. Finally, we demonstrate its use on a collection of music genre taxonomies.

Supply and demand of independent UK music artists on the web

As in any dynamic market, supply and demand of music are in a constant state of disequilibrium. Music charts have for many years documented the demand for the most popular music, but a more comprehensive understanding of this market has remained beyond reach. In this paper, we provide a proof of concept for how web resources now make it possible to study both demand and supply sides, accounting also for smaller, independent artists.

Interactively Exploring Supply and Demand in the UK Independent Music Scene

We present an exploratory data mining tool useful for finding patterns in the geographic distribution of independent UK-based music artists. Our system is interactive, highly intuitive, and entirely browser-based, meaning it can be used without any additional software installations from any device. The target audiences are artists, other music professionals, and the general public. Potential uses of our software include highlighting discrepancies in supply and demand of specific music genres in different parts of the country, and identifying at a glance which areas have the highest densities of independent music artists.

Machine Learning and Knowledge Discovery in Databases, Part III

We present an exploratory data mining tool useful for finding patterns in the geographic distribution of independent UK-based music artists. Our system is interactive, highly intuitive, and entirely browser-based, meaning it can be used without any additional software installations from any device. The target audiences are artists, other music professionals, and the general public. Potential uses of our software include highlighting discrepancies in supply and demand of specific music genres in different parts of the country, and identifying at a glance which areas have the highest densities of independent music artists.

Interactively exploring supply and demand in the UK independent music scene

We present an exploratory data mining tool useful for finding patterns in the geographic distribution of independent UK-based music artists. Our system is interactive, highly intuitive, and entirely browser-based, meaning it can be used without any additional software installations from any device. The target audiences are artists, other music professionals, and the general public. Potential uses of our software include highlighting discrepancies in supply and demand of specific music genres in different parts of the country, and identifying at a glance which areas have the highest densities of independent music artists.