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

Advanced Music Audio Feature Learning with Deep Networks

Music is a means of reflecting and expressing emotion. Personal preferences in music vary between individuals, influenced by situational and environmental factors. Inspired by attempts to develop alternative feature extraction methods for audio signals, this research analyzes the use of deep network structures for extracting features from musical audio data represented in the frequency domain. Image-based network models are designed to be robust and accurate learners of image features. As such, this research develops image-based ImageNet deep network models to learn feature data from music audio spectrograms. This research also explores the use of an audio source separation tool for preprocessing the musical audio before training the network models. The use of source separation allows the network model to learn features that highlight individual contributions to the audio track, and use those features to improve classification results. The features extracted from the data are used to highlight characteristics of the audio tracks, which are then used to train classifiers that categorize the musical data for genre and auto-tag classifications. The results obtained from each model are contrasted with state-of-the-art methods of classification and tag prediction for musical tracks. Deeper networks with input source separation are shown to yield the best results

A survey on artificial intelligence-based acoustic source identification

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions

Lauluyhtyeen intonaation automaattinen määritys

The objective of this study is a specific music signal processing task, primarily intended to help vocal ensemble singers practice their intonation. In this case intonation is defined as deviations of pitch in relation to the note written in the score which are small, less than a semitone. These can be either intentional or unintentional. Practicing intonation is typically challenging without an external ear. The algorithm developed in this thesis combined with the presented application concept can act as the external ear, providing real-time information on intonation to support practicing. The method can be applied to the analysis of recorded material as well. The music signal generated by a vocal ensemble is polyphonic. It contains multiple simultaneous tones with partly or completely overlapping harmonic partials. We need to be able to estimate the fundamental frequency of each tone, which then indicates the pitch of each singer. Our experiments show, that the fundamental frequency estimation method based on the Fourier analysis developed in this thesis can be applied to the automatic analysis of vocal ensembles. A sufficient frequency resolution can be achieved without compromising the time resolution too much by using an adequately sized window. The accuracy and robustness can be further increased by taking advantage of solitary partials. The greatest challenge turned out to be the estimation of tones in octave and unison relationships. These intervals are fairly common in tonal music. This question requires further investigation or another type of approach.Tässä työssä tutkitaan erityistä musiikkisignaalin analysointitehtävää, jonka tarkoi- tuksena on auttaa lauluyhtyelaulajia intonaation harjoittelussa. Intonaatiolla tar- koitetaan tässä yhteydessä pieniä, alle puolen sävelaskeleen säveltasoeroja nuottiin kirjoitettuun sävelkorkeuteen nähden, jotka voivat olla joko tarkoituksenmukaisia tai tahattomia. Intonaation harjoittelu on tyypillisesti haastavaa ilman ulkopuolista korvaa. Työssä kehitetty algoritmi yhdessä esitellyn sovelluskonseptin kanssa voi toimia harjoittelutilanteessa ulkopuolisena korvana tarjoten reaaliaikaista tietoa intonaatiosta harjoittelun tueksi. Vaihtoehtoisesti menetelmää voidaan hyödyntää harjoitusäänitteiden analysointiin jälkikäteen. Lauluyhtyeen tuottama musiikki- signaali on polyfoninen. Se sisältää useita päällekkäisiä säveliä, joiden osasävelet menevät toistensa kanssa osittain tai kokonaan päällekkäin. Tästä signaalista on pystyttävä tunnistamaan kunkin sävelen perustaajuus, joka puolestaan kertoo lau- lajan laulaman sävelkorkeuden. Kokeellisten tulosten perusteella työssä kehitettyä Fourier-muunnokseen perustuvaa taajuusanalyysiä voidaan soveltaa lauluyhtyeen intonaation automaattiseen määritykseen, kun nuottiin kirjoitettua sointua hyödyn- netään analyysin lähtötietona. Sopivankokoista näyteikkunaa käyttämällä päästiin riittävään taajuusresoluutioon aikaresoluution säilyessä kohtuullisena. Yksinäisiä osasäveliä hyödyntämällä voidaan edelleen parantaa tarkkuutta ja toimintavar- muutta. Suurimmaksi haasteeksi osoittautui oktaavi- ja priimisuhteissa olevien intervallien luotettava määritys. Näitä intervallisuhteita esiintyy tonaalisessa musii- kissa erityisen paljon. Tämä kysymys vaatii vielä lisätutkimusta tai uudenlaista lähestymistapaa