A cartesian ensemble of feature subspace classifiers for music categorization

We present a cartesian ensemble classification system that is based on the principle of late fusion and feature subspaces. These feature subspaces describe different aspects of the same data set. The framework is built on the Weka machine learning toolkit and able to combine arbitrary feature sets and learning schemes. In our scenario, we use it for the ensemble classification of multiple feature sets from the audio and symbolic domains. We present an extensive set of experiments in the context of music genre classification, based on numerous Music IR benchmark datasets, and evaluate a set of combination/voting rules. The results show that the approach is superior to the best choice of a single algorithm on a single feature set. Moreover, it also releases the user from making this choice explicitly.International Society for Music Information Retrieva

The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use

The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our recent work, however, shows GTZAN has several faults (repetitions, mislabelings, and distortions), which challenge the interpretability of any result derived using it. In this article, we disprove the claims that all MGR systems are affected in the same ways by these faults, and that the performances of MGR systems in GTZAN are still meaningfully comparable since they all face the same faults. We identify and analyze the contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has been used in MGR research, and find few indications that its faults have been known and considered. Finally, we rigorously study the effects of its faults on evaluating five different MGR systems. The lesson is not to banish GTZAN, but to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference

A supervised classification approach for note tracking in polyphonic piano transcription

In the field of Automatic Music Transcription, note tracking systems constitute a key process in the overall success of the task as they compute the expected note-level abstraction out of a frame-based pitch activation representation. Despite its relevance, note tracking is most commonly performed using a set of hand-crafted rules adjusted in a manual fashion for the data at issue. In this regard, the present work introduces an approach based on machine learning, and more precisely supervised classification, that aims at automatically inferring such policies for the case of piano music. The idea is to segment each pitch band of a frame-based pitch activation into single instances which are subsequently classified as active or non-active note events. Results using a comprehensive set of supervised classification strategies on the MAPS piano data-set report its competitiveness against other commonly considered strategies for note tracking as well as an improvement of more than +10% in terms of F-measure when compared to the baseline considered for both frame-level and note-level evaluations.This research work is partially supported by Universidad de Alicante through the FPU program [UAFPU2014–5883] and the Spanish Ministerio de Economía y Competitividad through project TIMuL [No. TIN2013–48152–C2–1–R, supported by EU FEDER funds]. EB is supported by a UK RAEng Research Fellowship [grant number RF/128]

Recuperación de la información musical por similitud usando redes neuronales

En los últimos años, la distribución de música digital en la web ha permitido a los usuarios acceder a grandes cantidades de información musical, con ello surge la necesidad de obtener esa información de manera eficaz y eficiente. En la actualidad, los sistemas de recuperación han ayudado a los usuarios a encontrar información basada en texto, pero esos modelos tradicionales no son adecuados si deseamos encontrar canciones que se parezcan en contenido de audio, de allí la necesidad de modelar e implementar métodos de recuperación basado en audio musical. En este estudio se describe un sistema que permite recuperar y clasificar canciones por similitud basado en contenido de audio musical. Se aplica un modelo de red neuronal a características de canciones. Primero se obtiene descriptores de canciones polifónicas en formato mp3 con características tales como: Análisis Espectral, Patrones de ritmo, Histograma de ritmo. Segundo, se realiza un análisis estadístico para seleccionar los descriptores válidos. Finalmente se ingresa a una red neuronal estos descriptores y se entrena. El objetivo de este trabajo es implementar el sistema y determinar, a partir de los resultados experimentales, la eficiencia de acierto o no para clasificar y recuperar contenido de audio musical por similitud.Tesi

Subgenre classification of rock music using support

Käesolev töö keskendub rokkmuusika alastiilide automaatsele klassifitseerimisele. Töö eesmärk on näha, kui edukalt on seda võimalik teha ning kas alastiilide klassifitseerimisel on tulevikuks potentsiaali. Ülesande lahendamiseks valiti tugivektormasinate meetod. Töös on antud ülevaade eraldatud tunnustest, kasutatud alastiili gruppidest ja tugivektormasinate tööpõhimõttest. Selle töö eesmärgil koostati muusikakorpus, mis koosnes viiest alastiilide grupist. Nendeks gruppideks olid: progressiivne rokk, punkrokk, metal-muusika, ekstreem-metal ja klassikaline rokkmuusika. Tööks kasutati 500 lugu, millest 400 olid kasutusel mudeli treenimiseks ja 100 testimiseks. Tunnuste eraldamiseks kasutati jAudio võimalusi ja klassifitseerimiseks kasutati Wekat. Suurimaks klassifitseerimise täpsuseks saavutati 71%. Kvartiilhaaret kasutades saavutati 74% täpsust.This paper focuses on performing automatic genre classification using subgenres of rock music. The purpose of this paper is to see how well it can be done and whether subgenre classification has potential for the future. Suport vector machines were chosen for this task. Overviews of the extracted features, used genre groups, and the basic ideas behind support vector machines are presented. For the purpose of this work, a dataset of five different subgenre groups was constructed. The groups were as follows: progressive rock, punk rock, general metal, extreme metal, and general rock music. A total of 500 songs was used, of which 400 songs was used to train the model and 100 songs was use to test it. Features were extracted using jAudio and classification task was done with Weka. Highest result achieved was the classification acuracy of 71%. With the use of interquartile ranges the accuracy reached 74%