Automatic music transcription: challenges and future directions

Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects

Music Information Retrieval Meets Music Education

This paper addresses the use of Music Information Retrieval (MIR) techniques in music education and their integration in learning software. A general overview of systems that are either commercially available or in research stage is presented. Furthermore, three well-known MIR methods used in music learning systems and their state-of-the-art are described: music transcription, solo and accompaniment track creation, and generation of performance instructions. As a representative example of a music learning system developed within the MIR community, the Songs2See software is outlined. Finally, challenges and directions for future research are described

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

VGM-RNN: Recurrent Neural Networks for Video Game Music Generation

The recent explosion of interest in deep neural networks has affected and in some cases reinvigorated work in fields as diverse as natural language processing, image recognition, speech recognition and many more. For sequence learning tasks, recurrent neural networks and in particular LSTM-based networks have shown promising results. Recently there has been interest – for example in the research by Google’s Magenta team – in applying so-called “language modeling” recurrent neural networks to musical tasks, including for the automatic generation of original music. In this work we demonstrate our own LSTM-based music language modeling recurrent network. We show that it is able to learn musical features from a MIDI dataset and generate output that is musically interesting while demonstrating features of melody, harmony and rhythm. We source our dataset from VGMusic.com, a collection of user-submitted MIDI transcriptions of video game songs, and attempt to generate output which emulates this kind of music

Chord-Conditioned Melody Choralization with Controllable Harmonicity and Polyphonicity

Melody choralization, i.e. generating a four-part chorale based on a user-given melody, has long been closely associated with J.S. Bach chorales. Previous neural network-based systems rarely focus on chorale generation conditioned on a chord progression, and none of them realised controllable melody choralization. To enable neural networks to learn the general principles of counterpoint from Bach's chorales, we first design a music representation that encoded chord symbols for chord conditioning. We then propose DeepChoir, a melody choralization system, which can generate a four-part chorale for a given melody conditioned on a chord progression. Furthermore, with the improved density sampling, a user can control the extent of harmonicity and polyphonicity for the chorale generated by DeepChoir. Experimental results reveal the effectiveness of our data representation and the controllability of DeepChoir over harmonicity and polyphonicity. The code and generated samples (chorales, folk songs and a symphony) of DeepChoir, and the dataset we use now are available at https://github.com/sander-wood/deepchoir.Comment: 7 pages, 4 figures, 2 table