Applications of analysis and synthesis techniques for complex sounds

Master'sMASTER OF SCIENC

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 content analysis: Key, chord and rhythm tracking in acoustic signals

Master'sMASTER OF SCIENC

Ontology of music performance variation

Performance variation in rhythm determines the extent that humans perceive and feel the effect of rhythmic pulsation and music in general. In many cases, these rhythmic variations can be linked to percussive performance. Such percussive performance variations are often absent in current percussive rhythmic models. The purpose of this thesis is to present an interactive computer model, called the PD-103, that simulates the micro-variations in human percussive performance. This thesis makes three main contributions to existing knowledge: firstly, by formalising a new method for modelling percussive performance; secondly, by developing a new compositional software tool called the PD-103 that models human percussive performance, and finally, by creating a portfolio of different musical styles to demonstrate the capabilities of the software. A large database of recorded samples are classified into zones based upon the vibrational characteristics of the instruments, to model timbral variation in human percussive performance. The degree of timbral variation is governed by principles of biomechanics and human percussive performance. A fuzzy logic algorithm is applied to analyse current and first-order sample selection in order to formulate an ontological description of music performance variation. Asynchrony values were extracted from recorded performances of three different performance skill levels to create \timing fingerprints" which characterise unique features to each percussionist. The PD-103 uses real performance timing data to determine asynchrony values for each synthesised note. The spectral content of the sample database forms a three-dimensional loudness/timbre space, intersecting instrumental behaviour with music composition. The reparameterisation of the sample database, following the analysis of loudness, spectral flatness, and spectral centroid, provides an opportunity to explore the timbral variations inherent in percussion instruments, to creatively explore dimensions of timbre. The PD-103 was used to create a music portfolio exploring different rhythmic possibilities with a focus on meso-periodic rhythms common to parts of West Africa, jazz drumming, and electroacoustic music. The portfolio also includes new timbral percussive works based on spectral features and demonstrates the central aim of this thesis, which is the creation of a new compositional software tool that integrates human percussive performance and subsequently extends this model to different genres of music

SIGNAL TRANSFORMATIONS FOR IMPROVING INFORMATION REPRESENTATION, FEATURE EXTRACTION AND SOURCE SEPARATION

Questa tesi riguarda nuovi metodi di rappresentazione del segnale nel dominio tempo-frequenza, tali da mostrare le informazioni ricercate come dimensioni esplicite di un nuovo spazio. In particolare due trasformate sono introdotte: lo Spazio di Miscelazione Bivariato (Bivariate Mixture Space) e il Campo della Struttura Spettro-Temporale (Spectro-Temporal Structure-Field). La prima trasformata mira a evidenziare le componenti latenti di un segnale bivariato basandosi sul comportamento di ogni componente frequenziale (ad esempio a fini di separazione delle sorgenti); la seconda trasformata mira invece all'incapsulamento di informazioni relative al vicinato di un punto in R^2 in un vettore associato al punto stesso, tale da descrivere alcune propriet\ue0 topologiche della funzione di partenza. Nel dominio dell'elaborazione digitale del segnale audio, il Bivariate Mixture Space pu\uf2 essere interpretato come un modo di investigare lo spazio stereofonico per operazioni di separazione delle sorgenti o di estrazione di informazioni, mentre lo Spectro-Temporal Structure-Field pu\uf2 essere usato per ispezionare lo spazio spettro-temporale (segregare suoni percussivi da suoni intonati o tracciae modulazioni di frequenza). Queste trasformate sono studiate e testate anche in relazione allo stato del'arte in campi come la separazione delle sorgenti, l'estrazione di informazioni e la visualizzazione dei dati. Nel campo dell'informatica applicata al suono, queste tecniche mirano al miglioramento della rappresentazione del segnale nel dominio tempo-frequenza, in modo tale da rendere possibile l'esplorazione dello spettro anche in spazi alternativi, quali il panorama stereofonico o una dimensione virtuale che separa gli aspetti percussivi da quelli intonati.This thesis is about new methods of signal representation in time-frequency domain, so that required information is rendered as explicit dimensions in a new space. In particular two transformations are presented: Bivariate Mixture Space and Spectro-Temporal Structure-Field. The former transform aims at highlighting latent components of a bivariate signal based on the behaviour of each frequency base (e.g. for source separation purposes), whereas the latter aims at folding neighbourhood information of each point of a R^2 function into a vector, so as to describe some topological properties of the function. In the audio signal processing domain, the Bivariate Mixture Space can be interpreted as a way to investigate the stereophonic space for source separation and Music Information Retrieval tasks, whereas the Spectro-Temporal Structure-Field can be used to inspect spectro-temporal dimension (segregate pitched vs. percussive sounds or track pitch modulations). These transformations are investigated and tested against state-of-the-art techniques in fields such as source separation, information retrieval and data visualization. In the field of sound and music computing, these techniques aim at improving the frequency domain representation of signals such that the exploration of the spectrum can be achieved also in alternative spaces like the stereophonic panorama or a virtual percussive vs. pitched dimension

Étude de transformées temps-fréquence pour le codage audio faible retard en haute qualité

In recent years there has been a phenomenal increase in the number of products and applications which make use of audio coding formats. Amongthe most successful audio coding schemes, the MPEG-1 Layer III (mp3), the MPEG-2 Advanced Audio Coding (AAC) or its evolution MPEG-4High Efficiency-Advanced Audio Coding (HE-AAC) can be cited. More recently, perceptual audio coding has been adapted to achieve codingat low-delay such to become suitable for conversational applications. Traditionally, the use of filter bank such as the Modified Discrete CosineTransform (MDCT) is a central component of perceptual audio coding and its adaptation to low delay audio coding has become an important researchtopic. Low delay transforms have been developed in order to retain the performance of standard audio coding while reducing dramatically the associated algorithmic delay.This work presents some elements allowing to better accommodate the delay reduction constraint. Among the contributions, a low delay blockswitching tool which allows the direct transition between long transform and short transform without the insertion of transition window. The sameprinciple has been extended to define new perfect reconstruction conditions for the MDCT with relaxed constraints compared to the original definition.As a consequence, a seamless reconstruction method has been derived to increase the flexibility of transform coding schemes with the possibility toselect a transform for a frame independently from its neighbouring frames. Finally, based on this new approach, a new low delay window design procedure has been derived to obtain an analytic definition for a new family of transforms, permitting high quality with a substantial coding delay reduction. The performance of the proposed transforms has been thoroughly evaluated, an evaluation framework involving an objective measurement of the optimal transform sequence is proposed. It confirms the relevance of the proposed transforms used for audio coding. In addition, the new approaches have been successfully applied to the recent standardisation work items, such as the low delay audio coding developed at MPEG (LD-AAC and ELD-AAC) and they have been evaluated with numerous subjective testing, showing a significant improvement of the quality for transient signals. The new low delay window design has been adopted in G.718, a scalable speech and audio codec standardized in ITU-T and has demonstrated its benefit in terms of delay reduction while maintaining the audio quality of a traditional MDCT.Codage audio à faible retard à l'aide de la définition de nouvelles fenêtres pour la transformée MDCT et l'introduction d'un nouveau schéma de commutation de fenêtre

Computer Models for Musical Instrument Identification

PhDA particular aspect in the perception of sound is concerned with what is commonly termed as texture or timbre. From a perceptual perspective, timbre is what allows us to distinguish sounds that have similar pitch and loudness. Indeed most people are able to discern a piano tone from a violin tone or able to distinguish different voices or singers. This thesis deals with timbre modelling. Specifically, the formant theory of timbre is the main theme throughout. This theory states that acoustic musical instrument sounds can be characterised by their formant structures. Following this principle, the central point of our approach is to propose a computer implementation for building musical instrument identification and classification systems. Although the main thrust of this thesis is to propose a coherent and unified approach to the musical instrument identification problem, it is oriented towards the development of algorithms that can be used in Music Information Retrieval (MIR) frameworks. Drawing on research in speech processing, a complete supervised system taking into account both physical and perceptual aspects of timbre is described. The approach is composed of three distinct processing layers. Parametric models that allow us to represent signals through mid-level physical and perceptual representations are considered. Next, the use of the Line Spectrum Frequencies as spectral envelope and formant descriptors is emphasised. Finally, the use of generative and discriminative techniques for building instrument and database models is investigated. Our system is evaluated under realistic recording conditions using databases of isolated notes and melodic phrases

Culturally sensitive strategies for automatic music prediction

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-112).Music has been shown to form an essential part of the human experience-every known society engages in music. However, as universal as it may be, music has evolved into a variety of genres, peculiar to particular cultures. In fact people acquire musical skill, understanding, and appreciation specific to the music they have been exposed to. This process of enculturation builds mental structures that form the cognitive basis for musical expectation. In this thesis I argue that in order for machines to perform musical tasks like humans do, in particular to predict music, they need to be subjected to a similar enculturation process by design. This work is grounded in an information theoretic framework that takes cultural context into account. I introduce a measure of musical entropy to analyze the predictability of musical events as a function of prior musical exposure. Then I discuss computational models for music representation that are informed by genre-specific containers for musical elements like notes. Finally I propose a software framework for automatic music prediction. The system extracts a lexicon of melodic, or timbral, and rhythmic primitives from audio, and generates a hierarchical grammar to represent the structure of a particular musical form. To improve prediction accuracy, context can be switched with cultural plug-ins that are designed for specific musical instruments and genres. In listening experiments involving music synthesis a culture-specific design fares significantly better than a culture-agnostic one. Hence my findings support the importance of computational enculturation for automatic music prediction. Furthermore I suggest that in order to sustain and cultivate the diversity of musical traditions around the world it is indispensable that we design culturally sensitive music technology.by Mihir Sarkar.Ph.D