Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.

A Survey of Audio-Based Music Classification and Annotation

The proliferation of MP3 players and the exploding amount of digital music content call for novel ways of music organization and retrieval to meet the ever-increasing demand for easy and effective information access. As almost every music piece is created to convey emotion, music organization and retrieval by emotion is a reasonable way of accessing music information. A good deal of effort has been made in the music information retrieval community to train a machine to automatically recognize the emotion of a music signal. A central issue of machine recognition of music emotion is the conceptualization of emotion and the associated emotion taxonomy. Different viewpoints on this issue have led to the proposal of different ways of emotion annotation, model training, and result visualization. This article provides a comprehensive review of the methods that have been proposed for music emotion recognition. Moreover, as music emotion recognition is still in its infancy, there are many open issues. We review the solutions that have been proposed to address these issues and conclude with suggestions for further research.

https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Yang%20-%20Machine%20Recognition%20of%20Music%20Emotion%20-%202013.pdf

Machine Recognition of Music Emotion: A Review

https://neuroaestheticsnet.files.wordpress.com/2012/06/alluri-2012.pdf

Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm.

Actualmente, el consumo de musica esta sesgada hacia algunos artistas muy populares. Por ejemplo, en el ano 2007 solo el 1% de todas las canciones en formato digital representaron el 80% de las ventas.
De igual modo, unicamente 1.000 albumes representaron el 50% de todas las ventas, y el 80% de todos los albumes vendidos se compraron menos de 100 veces. Existe, pues, una necesidad de ayudar a los usuarios a filtrar, descubrir, personalizar y recomendar musica a partir de la enorme cantidad de contenido musical existente.
Los algoritmos de recomendacion musical existentes intentan predecir con precision lo que la gente quiere escuchar. Sin embargo, muy a menudo estos algoritmos tienden a recomendar o bien artistas famosos, o bien artistas ya conocidos de antemano por el usuario. Esto disminuye la eficacia y la utilidad de las recomendaciones, ya que estos algoritmos se centran en mejorar la precision de las recomendaciones. Con lo cual, tratan de predecir lo que un usuario pudiera escuchar o comprar, independientemente de lo utiles que sean las recomendaciones generadas.
En este sentido, la tesis destaca la importancia de que el usuario valore las recomendaciones propuestas. Por ello, modelamos la curva de popularidad de los artistas con el fin de recomendar musica interesante y, a la vez, desconocida para el usuario. Las principales contribuciones de esta tesis son: (i) un nuevo enfoque basado en el analisis de redes complejas y la popularidad de los productos, aplicada a los sistemas de recomendacion, (ii) una evaluacion centrada en el usuario que mide la calidad y la novedad de las recomendaciones, y (iii) dos prototipos que implementan las ideas derivadas de la labor teorica. 
Los resultados obtenidos tienen importantes implicaciones para los sistemas de recomendacion que ayudan al usuario a explorar y descubrir contenidos que le puedan gustar.

Music recommendation and discovery in the long tail

We present a new technique for audio signal comparison based on tonal subsequence alignment and its application to detect cover versions (i.e., different performances of the same underlying musical piece). Cover song identification is a task whose popularity has increased in the music information retrieval (MIR) community along in the past, as it provides a direct and objective way to evaluate music similarity algorithms. This paper first presents a series of experiments carried out with two state-of-the-art methods for cover song identification. We have studied several components of these (such as chroma resolution and similarity, transposition, beat tracking or dynamic time warping constraints), in order to discover which characteristics would be desirable for a competitive cover song identifier. After analyzing many cross-validated results, the importance of these characteristics is discussed, and the best performing ones are finally applied to the newly proposed method. Multiple evaluations of this one confirm a large increase in identification accuracy when comparing it with alternative state-of-the-art approaches.

/pdf/chroma-binary-similarity-and-local-alignment-applied-to-2rju2qiy5b.pdf

Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification

This doctoral dissertation proposes and evaluates a computational approach for the automatic description of tonal aspects of music from the analysis of polyphonic audio signals. These algorithms focus on the computation of pitch class distributions descriptors, the estimation of the key of a piece, the visualization of the evolution of its tonal center or the measurement of the similarity between two different musical pieces.
This dissertation substantially contributes to the field of computational tonal description: a) It provides a multidisciplinary review of tonal induction systems; b) It defines a set of requirements for low-level tonal features; c) It provides a quantitative and modular evaluation of the proposed methods; d) It contributes to bridge the gap between audio and symbolic-oriented methods without the need of a perfect transcription; e) It extents current literature dealing with classical music to other musical genres; f) It shows the usefulness of tonal descriptors for music similarity; g) It provides an optimized method which is used in a real system for music visualization and retrieval, working with over a million of musical pieces.

Emilia Gómez Gutiérrez

Papers

Tonal description of music audio signals