Bayesian methods in music modelling

This thesis presents several hierarchical generative Bayesian models of musical signals designed to improve the accuracy of existing multiple pitch detection systems and other musical signal processing applications whilst remaining feasible for real-time computation. At the lowest level the signal is modelled as a set of overlapping sinusoidal basis functions. The parameters of these basis functions are built into a prior framework based on principles known from musical theory and the physics of musical instruments. The model of a musical note optionally includes phenomena such as frequency and amplitude modulations, damping, volume, timbre and inharmonicity. The occurrence of note onsets in a performance of a piece of music is controlled by an underlying tempo process and the alignment of the timings to the underlying score of the music. A variety of applications are presented for these models under differing inference constraints. Where full Bayesian inference is possible, reversible-jump Markov Chain Monte Carlo is employed to estimate the number of notes and partial frequency components in each frame of music. We also use approximate techniques such as model selection criteria and variational Bayes methods for inference in situations where computation time is limited or the amount of data to be processed is large. For the higher level score parameters, greedy search and conditional modes algorithms are found to be sufficiently accurate. We emphasize the links between the models and inference algorithms developed in this thesis with that in existing and parallel work, and demonstrate the effects of making modifications to these models both theoretically and by means of experimental results

Extending Deep Rhythm for Tempo and Genre Estimation Using Complex Convolutions, Multitask Learning and Multi-input Network

Tempo and genre are two inter-leaved aspects of music, genres are often associated to rhythm patterns which are played in specific tempo ranges. In this paper, we focus on the Deep Rhythm system based on a harmonic representation of rhythm used as an input to a convolutional neural network. To consider the relationships between frequency bands, we process complex-valued inputs through complex-convolutions. We also study the joint estimation of tempo/genre using a multitask learning approach. Finally, we study the addition of a second input convolutional branch to the system applied to a mel-spectrogram input dedicated to the timbre. This multi-input approach allows to improve the performances for tempo and genre estimation

Probabilistic models of contextual effects in Auditory Pitch Perception

Perception was recognised by Helmholtz as an inferential process whereby learned expectations about the environment combine with sensory experience to give rise to percepts. Expectations are flexible, built from past experiences over multiple time-scales. What is the nature of perceptual expectations? How are they learned? How do they affect perception? These are the questions I propose to address in this thesis. I focus on two important yet simple perceptual attributes of sounds whose perception is widely regarded as effortless and automatic : pitch and frequency. In a first study, I aim to propose a definition of pitch as the solution of a computational goal. Pitch is a fundamental and salient perceptual attribute of many behaviourally important sounds including speech and music. The effortless nature of its perception has led to the search for a direct physical correlate of pitch and for mechanisms to extract pitch from peripheral neural responses. I propose instead that pitch is the outcome of a probabilistic inference of an underlying periodicity in sounds given a learned statistical prior over naturally pitch-evoking sounds, explaining in a single model a wide range of psychophysical results. In two other psychophysical studies I study how and at what time-scales recent sensory history affects the perception of frequency shifts and pitch shifts. (1) When subjects are presented with ambiguous pitch shifts (using octave ambiguous Shepard tone pairs), I show that sensory history is used to leverage the ambiguity in a way that reflects expectations of spectro-temporal continuity of auditory scenes. (2) In delayed 2 tone frequency discrimination tasks, I explore the contraction bias : when asked to report which of two tones separated by brief silence is higher, subjects behave as though they hear the earlier tone ’contracted’ in frequency towards a combination of recently presented stimulus frequencies, and the mean of the overall distribution of tones used in the experiment. I propose that expectations - the statistical learning of the sampled stimulus distribution - are built online and combined with sensory evidence in a statistically optimal fashion. Models derived in the thesis embody the concept of perception as unconscious inference. The results support the view that even apparently primitive acoustic percepts may derive from subtle statistical inference, suggesting that such inferential processes operate at all levels across our sensory systems

Signal processing methods for beat tracking, music segmentation, and audio retrieval

The goal of music information retrieval (MIR) is to develop novel strategies and techniques for organizing, exploring, accessing, and understanding music data in an efficient manner. The conversion of waveform-based audio data into semantically meaningful feature representations by the use of digital signal processing techniques is at the center of MIR and constitutes a difficult field of research because of the complexity and diversity of music signals. In this thesis, we introduce novel signal processing methods that allow for extracting musically meaningful information from audio signals. As main strategy, we exploit musical knowledge about the signals\u27 properties to derive feature representations that show a significant degree of robustness against musical variations but still exhibit a high musical expressiveness. We apply this general strategy to three different areas of MIR: Firstly, we introduce novel techniques for extracting tempo and beat information, where we particularly consider challenging music with changing tempo and soft note onsets. Secondly, we present novel algorithms for the automated segmentation and analysis of folk song field recordings, where one has to cope with significant fluctuations in intonation and tempo as well as recording artifacts. Thirdly, we explore a cross-version approach to content-based music retrieval based on the query-by-example paradigm. In all three areas, we focus on application scenarios where strong musical variations make the extraction of musically meaningful information a challenging task.Ziel der automatisierten Musikverarbeitung ist die Entwicklung neuer Strategien und Techniken zur effizienten Organisation großer Musiksammlungen. Ein Schwerpunkt liegt in der Anwendung von Methoden der digitalen Signalverarbeitung zur Umwandlung von Audiosignalen in musikalisch aussagekräftige Merkmalsdarstellungen. Große Herausforderungen bei dieser Aufgabe ergeben sich aus der Komplexität und Vielschichtigkeit der Musiksignale. In dieser Arbeit werden neuartige Methoden vorgestellt, mit deren Hilfe musikalisch interpretierbare Information aus Musiksignalen extrahiert werden kann. Hierbei besteht eine grundlegende Strategie in der konsequenten Ausnutzung musikalischen Vorwissens, um Merkmalsdarstellungen abzuleiten die zum einen ein hohes Maß an Robustheit gegenüber musikalischen Variationen und zum anderen eine hohe musikalische Ausdruckskraft besitzen. Dieses Prinzip wenden wir auf drei verschieden Aufgabenstellungen an: Erstens stellen wir neuartige Ansätze zur Extraktion von Tempo- und Beat-Information aus Audiosignalen vor, die insbesondere auf anspruchsvolle Szenarien mit wechselnden Tempo und weichen Notenanfängen angewendet werden. Zweitens tragen wir mit neuartigen Algorithmen zur Segmentierung und Analyse von Feldaufnahmen von Volksliedern unter Vorliegen großer Intonationsschwankungen bei. Drittens entwickeln wir effiziente Verfahren zur inhaltsbasierten Suche in großen Datenbeständen mit dem Ziel, verschiedene Interpretationen eines Musikstückes zu detektieren. In allen betrachteten Szenarien richten wir unser Augenmerk insbesondere auf die Fälle in denen auf Grund erheblicher musikalischer Variationen die Extraktion musikalisch aussagekräftiger Informationen eine große Herausforderung darstellt

Towards the Automatic Analysis of Metric Modulations

PhDThe metrical structure is a fundamental aspect of music, yet its automatic analysis from audio recordings remains one of the great challenges of Music Information Retrieval (MIR) research. This thesis is concerned with addressing the automatic analysis of changes of metrical structure over time, i.e. metric modulations. The evaluation of automatic musical analysis methods is a critical element of the MIR research and is typically performed by comparing the machine-generated estimates with human expert annotations, which are used as a proxy for ground truth. We present here two new datasets of annotations for the evaluation of metrical structure and metric modulation estimation systems. Multiple annotations allowed for the assessment of inter-annotator (dis)agreement, thereby allowing for an evaluation of the reference annotations used to evaluate the automatic systems. The rhythmogram has been identified in previous research as a feature capable of capturing characteristics of rhythmic content of a music recording. We present here a direct evaluation of its ability to characterise the metrical structure and as a result we propose a method to explicitly extract metrical structure descriptors from it. Despite generally good and increasing performance, such rhythm features extraction systems occasionally fail. When unpredictable, the failures are a barrier to usability and development of trust in MIR systems. In a bid to address this issue, we then propose a method to estimate the reliability of rhythm features extraction. Finally, we propose a two-fold method to automatically analyse metric modulations from audio recordings. On the one hand, we propose a method to detect metrical structure changes from the rhythmogram feature in an unsupervised fashion. On the other hand, we propose a metric modulations taxonomy rooted in music theory that relies on metrical structure descriptors that can be automatically estimated. Bringing these elements together lays the ground for the automatic production of a musicological interpretation of metric modulations.EPSRC award 1325200 and Omnifone Ltd

Music-listening systems

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2000.Includes bibliographical references (p. [235]-248).When human listeners are confronted with musical sounds, they rapidly and automatically orient themselves in the music. Even musically untrained listeners have an exceptional ability to make rapid judgments about music from very short examples, such as determining the music's style, performer, beat, complexity, and emotional impact. However, there are presently no theories of music perception that can explain this behavior, and it has proven very difficult to build computer music-analysis tools with similar capabilities. This dissertation examines the psychoacoustic origins of the early stages of music listening in humans, using both experimental and computer-modeling approaches. The results of this research enable the construction of automatic machine-listening systems that can make human-like judgments about short musical stimuli. New models are presented that explain the perception of musical tempo, the perceived segmentation of sound scenes into multiple auditory images, and the extraction of musical features from complex musical sounds. These models are implemented as signal-processing and pattern-recognition computer programs, using the principle of understanding without separation. Two experiments with human listeners study the rapid assignment of high-level judgments to musical stimuli, and it is demonstrated that many of the experimental results can be explained with a multiple-regression model on the extracted musical features. From a theoretical standpoint, the thesis shows how theories of music perception can be grounded in a principled way upon psychoacoustic models in a computational-auditory-scene-analysis framework. Further, the perceptual theory presented is more relevant to everyday listeners and situations than are previous cognitive-structuralist approaches to music perception and cognition. From a practical standpoint, the various models form a set of computer signal-processing and pattern-recognition tools that can mimic human perceptual abilities on a variety of musical tasks such as tapping along with the beat, parsing music into sections, making semantic judgments about musical examples, and estimating the similarity of two pieces of music.Eric D. Scheirer.Ph.D

Spectral-based Features Ranking for Gamelan Instruments Identification using Filter Techniques

 In this paper, we describe an approach of spectral-based features ranking for Javanese gamelan instruments identification using filter techniques. The model extracted spectral-based features set of the signal using Short Time Fourier Transform (STFT). The rank of the features was determined using the five algorithms; namely ReliefF, Chi-Squared, Information Gain, Gain Ratio, and Symmetric Uncertainty. Then, we tested the ranked features by cross validation using Support Vector Machine (SVM). The experiment showed that Gain Ratio algorithm gave the best result, it yielded accuracy of 98.93%

Signal separation of musical instruments: simulation-based methods for musical signal decomposition and transcription

This thesis presents techniques for the modelling of musical signals, with particular regard to monophonic and polyphonic pitch estimation. Musical signals are modelled as a set of notes, each comprising of a set of harmonically-related sinusoids. An hierarchical model is presented that is very general and applicable to any signal that can be decomposed as the sum of basis functions. Parameter estimation is posed within a Bayesian framework, allowing for the incorporation of prior information about model parameters. The resulting posterior distribution is of variable dimension and so reversible jump MCMC simulation techniques are employed for the parameter estimation task. The extension of the model to time-varying signals with high posterior correlations between model parameters is described. The parameters and hyperparameters of several frames of data are estimated jointly to achieve a more robust detection. A general model for the description of time-varying homogeneous and heterogeneous multiple component signals is developed, and then applied to the analysis of musical signals. The importance of high level musical and perceptual psychological knowledge in the formulation of the model is highlighted, and attention is drawn to the limitation of pure signal processing techniques for dealing with musical signals. Gestalt psychological grouping principles motivate the hierarchical signal model, and component identifiability is considered in terms of perceptual streaming where each component establishes its own context. A major emphasis of this thesis is the practical application of MCMC techniques, which are generally deemed to be too slow for many applications. Through the design of efficient transition kernels highly optimised for harmonic models, and by careful choice of assumptions and approximations, implementations approaching the order of realtime are viable.Engineering and Physical Sciences Research Counci

Machine Annotation of Traditional Irish Dance Music

The work presented in this thesis is validated in experiments using 130 realworld field recordings of traditional music from sessions, classes, concerts and commercial recordings. Test audio includes solo and ensemble playing on a variety of instruments recorded in real-world settings such as noisy public sessions. Results are reported using standard measures from the field of information retrieval (IR) including accuracy, error, precision and recall and the system is compared to alternative approaches for CBMIR common in the literature