Interactive Manipulation of Musical Melody in Audio Recordings

The objective of this project is to develop an interactive technique to manipulate melody in musical recordings. The proposed methodology is based on the use of melody detection methods combined with the invertible constant Q transform (CQT), which allows a high-quality modification of musical content. This work will consist of several stages, the first of which will focus on monophonic recordings and subsequently we will explore methods to manipulate polyphonic recordings. The long-term objective is to alter a melody of a piece of music in such a way that it may sound similar to another. We have set, as and end goal, to allows users to perform melody manipulation and experiment with their music collection. To achieve this goal, we will devise approaches for high quality polyphonic melody manipulation, using a dataset of melodic content and mixed audio recordings. To ensure the system's usability, a listening test or user-study evaluation of the algorithm will be performed

Real-time Timbre Transfer and Sound Synthesis using DDSP

Neural audio synthesis is an actively researched topic, having yielded a wide range of techniques that leverages machine learning architectures. Google Magenta elaborated a novel approach called Differential Digital Signal Processing (DDSP) that incorporates deep neural networks with preconditioned digital signal processing techniques, reaching state-of-the-art results especially in timbre transfer applications. However, most of these techniques, including the DDSP, are generally not applicable in real-time constraints, making them ineligible in a musical workflow. In this paper, we present a real-time implementation of the DDSP library embedded in a virtual synthesizer as a plug-in that can be used in a Digital Audio Workstation. We focused on timbre transfer from learned representations of real instruments to arbitrary sound inputs as well as controlling these models by MIDI. Furthermore, we developed a GUI for intuitive high-level controls which can be used for post-processing and manipulating the parameters estimated by the neural network. We have conducted a user experience test with seven participants online. The results indicated that our users found the interface appealing, easy to understand, and worth exploring further. At the same time, we have identified issues in the timbre transfer quality, in some components we did not implement, and in installation and distribution of our plugin. The next iteration of our design will address these issues. Our real-time MATLAB and JUCE implementations are available at https://github.com/SMC704/juce-ddsp and https://github.com/SMC704/matlab-ddsp , respectively

Real-Time Polyphonic Octave Doubling for the Guitar

This thesis studies digital signal processing solutions for enriching live guitar sound by way of mixing-in octave-doubled versions of the chords and melodies performed on the instrument in real-time. Following a review of techniques applicable for real-time polyphonic octave doubling, four candidate solutions are proposed, amongst which two novel methods: ERB-PS2 and ERB-SSM2. Performance of said candidates is compared to that of three state of the art effect pedal offerings of the market. In particular, an evaluation of the added roughness and transient alterations introduced by each solution in the output sound is conducted. The ERB-PS2 method, which consists in doubling the instantaneous phases of the sub-bands signals extracted with a constant-ERB-bandwidth non-decimated IIR filter bank, is found to provide the best overall performance amongst the candidates. This novel solution provides greatly reduced latency compared to the baseline pedals, with comparable, and in some case improved, sound quality

Pitch shifting techniques for high-frequency passive sonar audio

Listening to passive sonar signals is a vital tool for sonar operators to classify underwater sound sources. While many passive sonar systems operate in the human auditory range (20 Hz to 20 kHz) there are a considerable number of high-frequency systems that extend beyond this range. This report examines pitch shifting algorithms for compressing ultrasonic, bandlimited passive sonar signals down into the auditory spectrum. By utilizing pitch shifting techniques the signal’s harmonic structure and length in time are retained. The frequency spectrum is lowered into the auditory range so that the sonar operator may then listen and characterize targets. Three pitch shifting algorithms are examined: Waveform Similarity Overlap-Add (WSOLA), Phase Vocoder, and Constant-Q Transform (CQT). Both synthetic and real sonar data is experimentally applied to each method and results are presented. Comparisons of performance are provided with an emphasis on feasibility for real-time sonar system implementation.Electrical and Computer Engineerin

Independent formant and pitch control applied to singing voice

Thesis (MScIng)--University of Stellenbosch, 2004.ENGLISH ABSTRACT: A singing voice can be manipulated artificially by means of a digital computer for the purposes of creating new melodies or to correct existing ones. When the fundamental frequency of an audio signal that represents a human voice is changed by simple algorithms, the formants of the voice tend to move to new frequency locations, making it sound unnatural. The main purpose is to design a technique by which the pitch and formants of a singing voice can be controlled independently.AFRIKAANSE OPSOMMING: Onafhanklike formant- en toonhoogte beheer toegepas op ’n sangstem: ’n Sangstem kan deur ’n digitale rekenaar gemanipuleer word om nuwe melodie¨e te skep, of om bestaandes te verbeter. Wanneer die fundamentele frekwensie van ’n klanksein (wat ’n menslike stem voorstel) deur ’n eenvoudige algoritme verander word, skuif die oorspronklike formante na nuwe frekwensie gebiede. Dit veroorsaak dat die resultaat onnatuurlik klink. Die hoof oogmerk is om ’n tegniek te ontwerp wat die toonhoogte en die formante van ’n sangstem apart kan beheer

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

Statistical Spectral Parameter Estimation of Acoustic Signals with Applications to Byzantine Music

Digitized acoustical signals of Byzantine music performed by Iakovos Nafpliotis are used to extract the fundamental frequency of each note of the diatonic scale. These empirical results are then contrasted to the theoretical suggestions and previous empirical findings. Several parametric and non-parametric spectral parameter estimation methods are implemented. These include: (1) Phase vocoder method, (2) McAulay-Quatieri method, (3) Levinson-Durbin algorithm,(4) YIN, (5) Quinn & Fernandes Estimator, (6) Pisarenko Frequency Estimator, (7) MUltiple SIgnal Characterization (MUSIC) algorithm, (8) Periodogram method, (9) Quinn & Fernandes Filtered Periodogram, (10) Rife & Vincent Estimator, and (11) the Fourier transform. Algorithm performance was very precise. The psychophysical aspect of human pitch discrimination is explored. The results of eight (8) psychoacoustical experiments were used to determine the aural just noticeable difference (jnd) in pitch and deduce patterns utilized to customize acceptable performable pitch deviation to the application at hand. These customizations [Acceptable Performance Difference (a new measure of frequency differential acceptability), Perceptual Confidence Intervals (a new concept of confidence intervals based on psychophysical experiment rather than statistics of performance data), and one based purely on music-theoretical asymphony] are proposed, discussed, and used in interpretation of results. The results suggest that Nafpliotis\u27 intervals are closer to just intonation than Byzantine theory (with minor exceptions), something not generally found in Thrasivoulos Stanitsas\u27 data. Nafpliotis\u27 perfect fifth is identical to the just intonation, even though he overstretches his octaveby fifteen (15)cents. His perfect fourth is also more just, as opposed to Stanitsas\u27 fourth which is directionally opposite. Stanitsas\u27 tendency to exaggerate the major third interval A4-F4 is still seen in Nafpliotis, but curbed. This is the only noteworthy departure from just intonation, with Nafpliotis being exactly Chrysanthian (the most exaggerated theoretical suggestion of all) and Stanitsas overstretching it even more than Nafpliotis and Chrysanth. Nafpliotis ascends in the second tetrachord more robustly diatonically than Stanitsas. The results are reported and interpreted within the framework of Acceptable Performance Differences