SynthTab: Leveraging Synthesized Data for Guitar Tablature Transcription

Guitar tablature is a form of music notation widely used among guitarists. It captures not only the musical content of a piece, but also its implementation and ornamentation on the instrument. Guitar Tablature Transcription (GTT) is an important task with broad applications in music education and entertainment. Existing datasets are limited in size and scope, causing state-of-the-art GTT models trained on such datasets to suffer from overfitting and to fail in generalization across datasets. To address this issue, we developed a methodology for synthesizing SynthTab, a large-scale guitar tablature transcription dataset using multiple commercial acoustic and electric guitar plugins. This dataset is built on tablatures from DadaGP, which offers a vast collection and the degree of specificity we wish to transcribe. The proposed synthesis pipeline produces audio which faithfully adheres to the original fingerings, styles, and techniques specified in the tablature with diverse timbre. Experiments show that pre-training state-of-the-art GTT model on SynthTab improves transcription accuracy in same-dataset tests. More importantly, it significantly mitigates overfitting problems of GTT models in cross-dataset evaluation.Comment: Submitted to ICASSP202

Automatic Transcription of Bass Guitar Tracks applied for Music Genre Classification and Sound Synthesis

Musiksignale bestehen in der Regel aus einer Überlagerung mehrerer Einzelinstrumente. Die meisten existierenden Algorithmen zur automatischen Transkription und Analyse von Musikaufnahmen im Forschungsfeld des Music Information Retrieval (MIR) versuchen, semantische Information direkt aus diesen gemischten Signalen zu extrahieren. In den letzten Jahren wurde häufig beobachtet, dass die Leistungsfähigkeit dieser Algorithmen durch die Signalüberlagerungen und den daraus resultierenden Informationsverlust generell limitiert ist. Ein möglicher Lösungsansatz besteht darin, mittels Verfahren der Quellentrennung die beteiligten Instrumente vor der Analyse klanglich zu isolieren. Die Leistungsfähigkeit dieser Algorithmen ist zum aktuellen Stand der Technik jedoch nicht immer ausreichend, um eine sehr gute Trennung der Einzelquellen zu ermöglichen. In dieser Arbeit werden daher ausschließlich isolierte Instrumentalaufnahmen untersucht, die klanglich nicht von anderen Instrumenten überlagert sind. Exemplarisch werden anhand der elektrischen Bassgitarre auf die Klangerzeugung dieses Instrumentes hin spezialisierte Analyse- und Klangsynthesealgorithmen entwickelt und evaluiert.Im ersten Teil der vorliegenden Arbeit wird ein Algorithmus vorgestellt, der eine automatische Transkription von Bassgitarrenaufnahmen durchführt. Dabei wird das Audiosignal durch verschiedene Klangereignisse beschrieben, welche den gespielten Noten auf dem Instrument entsprechen. Neben den üblichen Notenparametern Anfang, Dauer, Lautstärke und Tonhöhe werden dabei auch instrumentenspezifische Parameter wie die verwendeten Spieltechniken sowie die Saiten- und Bundlage auf dem Instrument automatisch extrahiert. Evaluationsexperimente anhand zweier neu erstellter Audiodatensätze belegen, dass der vorgestellte Transkriptionsalgorithmus auf einem Datensatz von realistischen Bassgitarrenaufnahmen eine höhere Erkennungsgenauigkeit erreichen kann als drei existierende Algorithmen aus dem Stand der Technik. Die Schätzung der instrumentenspezifischen Parameter kann insbesondere für isolierte Einzelnoten mit einer hohen Güte durchgeführt werden.Im zweiten Teil der Arbeit wird untersucht, wie aus einer Notendarstellung typischer sich wieder- holender Basslinien auf das Musikgenre geschlossen werden kann. Dabei werden Audiomerkmale extrahiert, welche verschiedene tonale, rhythmische, und strukturelle Eigenschaften von Basslinien quantitativ beschreiben. Mit Hilfe eines neu erstellten Datensatzes von 520 typischen Basslinien aus 13 verschiedenen Musikgenres wurden drei verschiedene Ansätze für die automatische Genreklassifikation verglichen. Dabei zeigte sich, dass mit Hilfe eines regelbasierten Klassifikationsverfahrens nur Anhand der Analyse der Basslinie eines Musikstückes bereits eine mittlere Erkennungsrate von 64,8 % erreicht werden konnte.Die Re-synthese der originalen Bassspuren basierend auf den extrahierten Notenparametern wird im dritten Teil der Arbeit untersucht. Dabei wird ein neuer Audiosynthesealgorithmus vorgestellt, der basierend auf dem Prinzip des Physical Modeling verschiedene Aspekte der für die Bassgitarre charakteristische Klangerzeugung wie Saitenanregung, Dämpfung, Kollision zwischen Saite und Bund sowie dem Tonabnehmerverhalten nachbildet. Weiterhin wird ein parametrischerAudiokodierungsansatz diskutiert, der es erlaubt, Bassgitarrenspuren nur anhand der ermittel- ten notenweisen Parameter zu übertragen um sie auf Dekoderseite wieder zu resynthetisieren. Die Ergebnisse mehrerer Hötest belegen, dass der vorgeschlagene Synthesealgorithmus eine Re- Synthese von Bassgitarrenaufnahmen mit einer besseren Klangqualität ermöglicht als die Übertragung der Audiodaten mit existierenden Audiokodierungsverfahren, die auf sehr geringe Bitraten ein gestellt sind.Music recordings most often consist of multiple instrument signals, which overlap in time and frequency. In the field of Music Information Retrieval (MIR), existing algorithms for the automatic transcription and analysis of music recordings aim to extract semantic information from mixed audio signals. In the last years, it was frequently observed that the algorithm performance is limited due to the signal interference and the resulting loss of information. One common approach to solve this problem is to first apply source separation algorithms to isolate the present musical instrument signals before analyzing them individually. The performance of source separation algorithms strongly depends on the number of instruments as well as on the amount of spectral overlap.In this thesis, isolated instrumental tracks are analyzed in order to circumvent the challenges of source separation. Instead, the focus is on the development of instrument-centered signal processing algorithms for music transcription, musical analysis, as well as sound synthesis. The electric bass guitar is chosen as an example instrument. Its sound production principles are closely investigated and considered in the algorithmic design.In the first part of this thesis, an automatic music transcription algorithm for electric bass guitar recordings will be presented. The audio signal is interpreted as a sequence of sound events, which are described by various parameters. In addition to the conventionally used score-level parameters note onset, duration, loudness, and pitch, instrument-specific parameters such as the applied instrument playing techniques and the geometric position on the instrument fretboard will be extracted. Different evaluation experiments confirmed that the proposed transcription algorithm outperformed three state-of-the-art bass transcription algorithms for the transcription of realistic bass guitar recordings. The estimation of the instrument-level parameters works with high accuracy, in particular for isolated note samples.In the second part of the thesis, it will be investigated, whether the sole analysis of the bassline of a music piece allows to automatically classify its music genre. Different score-based audio features will be proposed that allow to quantify tonal, rhythmic, and structural properties of basslines. Based on a novel data set of 520 bassline transcriptions from 13 different music genres, three approaches for music genre classification were compared. A rule-based classification system could achieve a mean class accuracy of 64.8 % by only taking features into account that were extracted from the bassline of a music piece.The re-synthesis of a bass guitar recordings using the previously extracted note parameters will be studied in the third part of this thesis. Based on the physical modeling of string instruments, a novel sound synthesis algorithm tailored to the electric bass guitar will be presented. The algorithm mimics different aspects of the instrument’s sound production mechanism such as string excitement, string damping, string-fret collision, and the influence of the electro-magnetic pickup. Furthermore, a parametric audio coding approach will be discussed that allows to encode and transmit bass guitar tracks with a significantly smaller bit rate than conventional audio coding algorithms do. The results of different listening tests confirmed that a higher perceptual quality can be achieved if the original bass guitar recordings are encoded and re-synthesized using the proposed parametric audio codec instead of being encoded using conventional audio codecs at very low bit rate settings

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

DMRN+18: Digital Music Research Network One-day Workshop 2023

DMRN+18: Digital Music Research Network One-day Workshop 2023 Queen Mary University of London Tuesday 19th December 2023 • Keynote speaker: Stefan Bilbao The Digital Music Research Network (DMRN) aims to promote research in the area of digital music, by bringing together researchers from UK and overseas universities, as well as industry, for its annual workshop. The workshop will include invited and contributed talks and posters. The workshop will be an ideal opportunity for networking with other people working in the area. Keynote speakers: Stefan Bilbao Tittle: Physics-based Audio: Sound Synthesis and Virtual Acoustics. Abstract: Any acoustically-produced sound produced must be the result of physical laws that describe the dynamics of a given system---always at least partly mechanical, and sometimes with an electronic element as well. One approach to the synthesis of natural acoustic timbres, thus, is through simulation, often referred to in this context as physical modelling, or physics-based audio. In this talk, the principles of physics-based audio, and the various different approaches to simulation are described, followed by a set of examples covering: various musical instrument types; the important related problem of the emulation of room acoustics or “virtual acoustics”; the embedding of instruments in a 3D virtual space; electromechanical effects; and also new modular instrument designs based on physical laws, but without a counterpart in the real world. Some more technical details follow, including the strengths, weaknesses and limitations of such methods, and pointers to some links to data-centred black-box approaches to sound generation and effects processing. The talk concludes with some musical examples and recent work on moving such algorithms to a real-time setting.. Bio: Stefan is a Professor (full) at Reid School of Music, University of Edinburgh, he is the Personal Chair of Acoustics and Audio Signal Processing, Music. He currently works on computational acoustics, for applications in sound synthesis and virtual acoustics. Special topics of interest include: Finite difference time domain methods, distributed nonlinear systems such as strings and plates, architectural acoustics, spatial audio in simulation, multichannel sound synthesis, and hardware and software realizations. More information on: https://www.acoustics.ed.ac.uk/group-members/dr-stefan-bilbao/ DMRN+18 is sponsored by The UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM); a leading PhD research programme aimed at the Music/Audio Technology and Creative Industries, based at Queen Mary University of London

Tablature Notation from Monophonic Guitar Audio Using CNN

Automatic Music Transcription for instruments with fretboards, such as the guitar, involves transcribing audio into either standard notation or tablature notation. Tablature notation provides a one-to-one mapping between the symbol for a note and the string-fret combination used to produce it, and is often preferred over standard notation for this reason. Detecting the string-fret combination used to produce a note involves pitch detection and string detection, which are usually performed in this order in existing approaches. This Master's Thesis focuses on electric guitar string detection from monophonic samples using a convolutional neural network (CNN). A dataset containing over 10000 guitar notes with a detectable fundamental frequency was collected from three electric guitars and feature engineered to extract spectrogram, Mel-spectrogram and constant-Q transform per sample. Three convolutional neural networks were trained, one on each feature, to detect the guitar string from which each original sample had originated. The models were subjected to 6-fold stratified cross-validation. A string detection accuracy of 0.932 was achieved with the model trained on the constant-Q transform data

DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models

Originating in the Renaissance and burgeoning in the digital era, tablatures are a commonly used music notation system which provides explicit representations of instrument fingerings rather than pitches. GuitarPro has established itself as a widely used tablature format and software enabling musicians to edit and share songs for musical practice, learning, and composition. In this work, we present DadaGP, a new symbolic music dataset comprising 26,181 song scores in the GuitarPro format covering 739 musical genres, along with an accompanying tokenized format well-suited for generative sequence models such as the Transformer. The tokenized format is inspired by event-based MIDI encodings, often used in symbolic music generation models. The dataset is released with an encoder/decoder which converts GuitarPro files to tokens and back. We present results of a use case in which DadaGP is used to train a Transformer-based model to generate new songs in GuitarPro format. We discuss other relevant use cases for the dataset (guitar-bass transcription, music style transfer and artist/genre classification) as well as ethical implications. DadaGP opens up the possibility to train GuitarPro score generators, fine-tune models on custom data, create new styles of music, AI-powered songwriting apps, and human-AI improvisation

Real-time software electric guitar audio transcription

Guitar audio transcription is the process of generating a human-interpretable musical score from guitar audio. The musical score is presented as guitar tablature, which indicates not only what notes are played, but where they are played on the guitar fretboard. Automatic transcription remains a challenge when dealing with polyphonic sounds. The guitar adds further ambiguity to the transcription problem because the same note can often be played in many ways. In this thesis work, a portable software architecture is presented for processing guitar audio in real time and providing a set of highly probable transcription solutions. Novel algorithms for performing polyphonic pitch detection and generating confidence values for transcription solutions (by which they are ranked) are also presented. Transcription solutions are generated for individual signal windows based on the output of the polyphonic pitch detection algorithm. Confidence values are generated for solutions by analyzing signal properties, fingering difficulty, and proximity to previous highest confidence solutions. The rules used for generating confidence values are based on expert knowledge of the instrument. Performance is measured in terms of algorithm accuracy, latency, and throughput. The correct result is ranked 2.08 (with the top rank being 0) for chords. The general case of various notes over time presents results that require qualitative analysis; the system in general is very susceptible to noise and has a difficult time distinguishing harmonics from actual fundamentals. By allowing the user to seed the system with a ground truth, correct recognition of future states is improved significantly in some cases. The sampling time is 250 ms with an average processing time of 110 ms, giving an average total latency of 360 ms. Throughput is 62.5 sample windows per second. Performance is not processor-bound, enabling high performance on a wide variety of personal computers