A Parametric Sound Object Model for Sound Texture Synthesis

This thesis deals with the analysis and synthesis of sound textures based on parametric sound objects. An overview is provided about the acoustic and perceptual principles of textural acoustic scenes, and technical challenges for analysis and synthesis are considered. Four essential processing steps for sound texture analysis are identifi ed, and existing sound texture systems are reviewed, using the four-step model as a guideline. A theoretical framework for analysis and synthesis is proposed. A parametric sound object synthesis (PSOS) model is introduced, which is able to describe individual recorded sounds through a fi xed set of parameters. The model, which applies to harmonic and noisy sounds, is an extension of spectral modeling and uses spline curves to approximate spectral envelopes, as well as the evolution of parameters over time. In contrast to standard spectral modeling techniques, this representation uses the concept of objects instead of concatenated frames, and it provides a direct mapping between sounds of diff erent length. Methods for automatic and manual conversion are shown. An evaluation is presented in which the ability of the model to encode a wide range of di fferent sounds has been examined. Although there are aspects of sounds that the model cannot accurately capture, such as polyphony and certain types of fast modulation, the results indicate that high quality synthesis can be achieved for many different acoustic phenomena, including instruments and animal vocalizations. In contrast to many other forms of sound encoding, the parametric model facilitates various techniques of machine learning and intelligent processing, including sound clustering and principal component analysis. Strengths and weaknesses of the proposed method are reviewed, and possibilities for future development are discussed

Learning Mid-Level Auditory Codes from Natural Sound Statistics

Interaction with the world requires an organism to transform sensory signals into representations in which behaviorally meaningful properties of the environment are made explicit. These representations are derived through cascades of neuronal processing stages in which neurons at each stage recode the output of preceding stages. Explanations of sensory coding may thus involve understanding how low-level patterns are combined into more complex structures. Although models exist in the visual domain to explain how mid-level features such as junctions and curves might be derived from oriented filters in early visual cortex, little is known about analogous grouping principles for mid-level auditory representations. We propose a hierarchical generative model of natural sounds that learns combina- tions of spectrotemporal features from natural stimulus statistics. In the first layer the model forms a sparse convolutional code of spectrograms using a dictionary of learned spectrotemporal kernels. To generalize from specific kernel activation patterns, the second layer encodes patterns of time-varying magnitude of multiple first layer coefficients. Because second-layer features are sensitive to combi- nations of spectrotemporal features, the representation they support encodes more complex acoustic patterns than the first layer. When trained on corpora of speech and environmental sounds, some second-layer units learned to group spectrotemporal features that occur together in natural sounds. Others instantiate opponency between dissimilar sets of spectrotemporal features. Such groupings might be instantiated by neurons in the auditory cortex, providing a hypothesis for mid-level neuronal computation.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216

A User-assisted Approach to Multiple Instrument Music Transcription

PhDThe task of automatic music transcription has been studied for several decades and is regarded as an enabling technology for a multitude of applications such as music retrieval and discovery, intelligent music processing and large-scale musicological analyses. It refers to the process of identifying the musical content of a performance and representing it in a symbolic format. Despite its long research history, fully automatic music transcription systems are still error prone and often fail when more complex polyphonic music is analysed. This gives rise to the question in what ways human knowledge can be incorporated in the transcription process. This thesis investigates ways to involve a human user in the transcription process. More specifically, it is investigated how user input can be employed to derive timbre models for the instruments in a music recording, which are employed to obtain instrument-specific (parts-based) transcriptions. A first investigation studies different types of user input in order to derive instrument models by means of a non-negative matrix factorisation framework. The transcription accuracy of the different models is evaluated and a method is proposed that refines the models by allowing each pitch of each instrument to be represented by multiple basis functions. A second study aims at limiting the amount of user input to make the method more applicable in practice. Different methods are considered to estimate missing non-negative basis functions when only a subset of basis functions can be extracted based on the user information. A method is proposed to track the pitches of individual instruments over time by means of a Viterbi framework in which the states at each time frame contain several candidate instrument-pitch combinations. A transition probability is employed that combines three different criteria: the frame-wise reconstruction error of each combination, a pitch continuity measure that favours similar pitches in consecutive frames, and an explicit activity model for each instrument. The method is shown to outperform other state-of-the-art multi-instrument tracking methods. Finally, the extraction of instrument models that include phase information is investigated as a step towards complex matrix decomposition. The phase relations between the partials of harmonic sounds are explored as a time-invariant property that can be employed to form complex-valued basis functions. The application of the model for a user-assisted transcription task is illustrated with a saxophone example.QMU

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

Automatic Drum Transcription and Source Separation

While research has been carried out on automated polyphonic music transcription, to-date the problem of automated polyphonic percussion transcription has not received the same degree of attention. A related problem is that of sound source separation, which attempts to separate a mixture signal into its constituent sources. This thesis focuses on the task of polyphonic percussion transcription and sound source separation of a limited set of drum instruments, namely the drums found in the standard rock/pop drum kit. As there was little previous research on polyphonic percussion transcription a broad review of music information retrieval methods, including previous polyphonic percussion systems, was also carried out to determine if there were any methods which were of potential use in the area of polyphonic drum transcription. Following on from this a review was conducted of general source separation and redundancy reduction techniques, such as Independent Component Analysis and Independent Subspace Analysis, as these techniques have shown potential in separating mixtures of sources. Upon completion of the review it was decided that a combination of the blind separation approach, Independent Subspace Analysis (ISA), with the use of prior knowledge as used in music information retrieval methods, was the best approach to tackling the problem of polyphonic percussion transcription as well as that of sound source separation. A number of new algorithms which combine the use of prior knowledge with the source separation abilities of techniques such as ISA are presented. These include sub-band ISA, Prior Subspace Analysis (PSA), and an automatic modelling and grouping technique which is used in conjunction with PSA to perform polyphonic percussion transcription. These approaches are demonstrated to be effective in the task of polyphonic percussion transcription, and PSA is also demonstrated to be capable of transcribing drums in the presence of pitched instruments

Auditory Streaming: Behavior, Physiology, and Modeling

Auditory streaming is a fundamental aspect of auditory perception. It refers to the ability to parse mixed acoustic events into meaningful streams where each stream is assumed to originate from a separate source. Despite wide interest and increasing scientific investigations over the last decade, the neural mechanisms underlying streaming still remain largely unknown. A simple example of this mystery concerns the streaming of simple tone sequences, and the general assumption that separation along the tonotopic axis is sufficient for stream segregation. However, this dissertation research casts doubt on the validity of this assumption. First, behavioral measures of auditory streaming in ferrets prove that they can be used as an animal model to study auditory streaming. Second, responses from neurons in the primary auditory cortex (A1) of ferrets show that spectral components that are well-separated in frequency produce comparably segregated responses along the tonotopic axis, no matter whether presented synchronously or consecutively, despite the substantial differences in their streaming percepts when measured psychoacoustically in humans. These results argue against the notion that tonotopic separation per se is a sufficient neural correlate of stream segregation. Thirdly, comparing responses during behavior to those during the passive condition, the temporal correlations of spiking activity between neurons belonging to the same stream display an increased correlation, while responses among neurons belonging to different streams become less correlated. Rapid task-related plasticity of neural receptive fields shows a pattern that is consistent with the changes in correlation. Taken together these results indicate that temporal coherence is a plausible neural correlate of auditory streaming. Finally, inspired by the above biological findings, we propose a computational model of auditory scene analysis, which uses temporal coherence as the primary criterion for predicting stream formation. The promising results of this dissertation research significantly advance our understanding of auditory streaming and perception