APHONIC: Adaptive thresholding for noise cancellation in smart mobile environments

We propose a signal-channel, adaptive threshold selection technique for binary mask construction, namely APHONIC, (AdaPtive tHreshOlding for NoIse Cancellation) for smart mobile environments. Using this mask, we introduce two noise cancellation techniques that perform robustly in the presence of real-world interfering signals that are typically encountered by mobile users: a violin busker, a subway and busy city square sounds. We demonstrate that when the power of the time-frequency components of the voice of a mobile user does not significantly overlap with the components of the interference signal, the threshold learning and noise cancellation techniques significantly improve the Signal-to-Interference Ratio (SIR) and the Signal-Distortion Ratio (SDR) of the recovered voice. When a mobile user\u27s speech is mixed with music or with the sounds of a city square, or subway station, the speech energy is captured by a few large magnitude coefficients and APHONIC improves the SIR by greater than 20dB and the SDR by up to 5dB. The robustness of the threshold selection step and the noise cancellation algorithms is evaluated using environments typically experienced by mobile phone users. Listening tests indicate that the interference signal is no longer audible in the denoised signals. We outline how this approach could be used in many mobile voice-driven applications

APHONIC: Adaptive Thresholding for Noise Cancellation in Smart Mobile Environments

We propose a signal-channel, adaptive threshold selection technique for binary mask construction, namely APHONIC, (AdaPtive tHreshOlding for NoIse Cancellation) for smart mobile environments. Using this mask, we introduce two noise cancellation techniques that perform robustly in the presence of real-world interfering signals that are typically encountered by mobile users: a violin busker, a subway and busy city square sounds. We demonstrate that when the power of the time-frequency components of the voice of a mobile user does not significantly overlap with the components of the interference signal, the threshold learning and noise cancellation techniques significantly improve the Signal-to-Interference Ratio (SIR) and the Signal-Distortion Ratio (SDR) of the recovered voice. When a mobile user\u27s speech is mixed with music or with the sounds of a city square, or subway station, the speech energy is captured by a few large magnitude coefficients and APHONIC improves the SIR by greater than 20dB and the SDR by up to 5dB. The robustness of the threshold selection step and the noise cancellation algorithms is evaluated using environments typically experienced by mobile phone users. Listening tests indicate that the interference signal is no longer audible in the denoised signals. We outline how this approach could be used in many mobile voice-driven applications

The Synchronized Short-Time-Fourier-Transform: Properties and Definitions for Multichannel Source Separation.

This paper proposes the use of a synchronized linear transform, the synchronized short-time-Fourier-transform (sSTFT), for time-frequency analysis of anechoic mixtures. We address the short comings of the commonly used time-frequency linear transform in multichannel settings, namely the classical short-time-Fourier-transform (cSTFT). We propose a series of desirable properties for the linear transform used in a multichannel source separation scenario: stationary invertibility, relative delay, relative attenuation, and finally delay invariant relative windowed-disjoint orthogonality (DIRWDO). Multisensor source separation techniques which operate in the time-frequency domain, have an inherent error unless consideration is given to the multichannel properties proposed in this paper. The sSTFT preserves these relationships for multichannel data. The crucial innovation of the sSTFT is to locally synchronize the analysis to the observations as opposed to a global clock. Improvement in separation performance can be achieved because assumed properties of the time-frequency transform are satisfied when it is appropriately synchronized. Numerical experiments show the sSTFT improves instantaneous subsample relative parameter estimation in low noise conditions and achieves good synthesis

Effect of System Load on Video Service Metrics

Model selection, in order to learn the mapping between the kernel metrics of a machine in a server cluster and a service quality metric on a client\u27s machine, has been addressed by directly applying Linear Regression (LR) to the observations. The popularity of the LR approach is due to: 1) its implementation efficiency; 2) its low computational complexity; and finally, 3) it generally captures the data relatively accurately. LR, can however, produce misleading results if the LR model does not characterize the system: this deception is due in part to its accuracy. In the client-server service modeling literature LR is applied to the server and client metrics without treating the load on the system as the cause for the excitation of the system. By contrast, in this paper, we propose a generative model for the server and client metrics and a hierarchical model to explain the mapping between them, which is cognizant of the effects of the load on the system. Evaluations using real traces support the following conclusions: The system load accounts for ≥ 50% of the energy of a high proportion of the client and server metric traces -modeling the load is crucial; the load signal is localized in the frequency domain: we can remove the load by deconvolution; There is a significant phase shift between both the kernel and the service-level metrics, which, coupled with the load, heavily biases the results obtained from out-of-the-box LR without any system identification pre-processing

Iterative Separation of Note Events from Single-Channel Polyphonic Recordings

This thesis is concerned with the separation of audio sources from single-channel polyphonic musical recordings using the iterative estimation and separation of note events. Each event is defined as a section of audio containing largely harmonic energy identified as coming from a single sound source. Multiple events can be clustered to form separated sources. This solution is a model-based algorithm that can be applied to a large variety of audio recordings without requiring previous training stages. The proposed system embraces two principal stages. The first one considers the iterative detection and separation of note events from within the input mixture. In every iteration, the pitch trajectory of the predominant note event is automatically selected from an array of fundamental frequency estimates and used to guide the separation of the event's spectral content using two different methods: time-frequency masking and time-domain subtraction. A residual signal is then generated and used as the input mixture for the next iteration. After convergence, the second stage considers the clustering of all detected note events into individual audio sources. Performance evaluation is carried out at three different levels. Firstly, the accuracy of the note-event-based multipitch estimator is compared with that of the baseline algorithm used in every iteration to generate the initial set of pitch estimates. Secondly, the performance of the semi-supervised source separation process is compared with that of another semi-automatic algorithm. Finally, a listening test is conducted to assess the audio quality and naturalness of the separated sources when they are used to create stereo mixes from monaural recordings. Future directions for this research focus on the application of the proposed system to other music-related tasks. Also, a preliminary optimisation-based approach is presented as an alternative method for the separation of overlapping partials, and as a high resolution time-frequency representation for digital signals

Constructing Time-Frequency Dictionaries for Source Separation via Time-Frequency Masking and Source Localisation

We describe a new localisation and source separation algorithm which is based upon the accurate construction of time-frequency spatial signatures. We present a technique for constructing time-frequency spatial signatures with the required accuracy. This algorithm for multi-channel source separation and localisation allows arbitrary placement of microphones yet achieves good performance. We demonstrate the efficacy of the technique using source location estimates and compare estimated time-frequency masks with the ideal 0 dB mask

Optophone design: optical-to-auditory vision substitution for the blind

An optophone is a device that turns light into sound for the benefit of blind people. The present project is intended to produce a general-purpose optophone to be worn on the head about the house and in the street, to give the wearer a detailed description in sound of the'scene he is facing. The device will therefore consist'of an'electronic camera, some signal-processing electronics, earphones`, and a battery. The two major problems are the derivation of (a) the most suitable mapping from images to sounds, and (b) an algorithm to perform the mapping in real'time on existing electronic components. This thesis concerns problem (a). Chapter 2 goes into the general scene-to-sound mapping problem in some detail'and presents the work of earlier investigators. Chapter 3 1- discusses the design of tests to evaluate the performance of candidate mappings. A theoretical performance test (TPT) is derived. Chapter 4 applies the TPT to the most obvious mapping, the cartesian piano transform. Chapter 5 applies the TPT to a mapping based on the cosine transform. Chapter 6 attempts to derive a mapping by principal component analysis, using the inaccuracies of human sight and hearing and the statistical properties of real scenes and sounds. Chapter 7 presents a complete scheme, implemented in software, for representing digitised colour scenes by audible digitised stereo sound. Chapter 8 tries to decide how'many numbers are required to specify a steady spectrum with no noticeable degradation. Chapter 9 looks'at a scheme designed to produce more natural-sounding sounds related to more meaningful portions of the scene. This scheme maps windows in the scene to steady spectral patterns of short duration, the location of the window being conveyed by simulated free-field listening. Chapter 10 gives detailed recommendations as to further work

Bayesian Variational Regularisation for Dark Matter Reconstruction with Uncertainty Quantification

Despite the great wealth of cosmological knowledge accumulated since the early 20th century, the nature of dark-matter, which accounts for ~85% of the matter content of the universe, remains illusive. Unfortunately, though dark-matter is scientifically interesting, with implications for our fundamental understanding of the Universe, it cannot be directly observed. Instead, dark-matter may be inferred from e.g. the optical distortion (lensing) of distant galaxies which, at linear order, manifests as a perturbation to the apparent magnitude (convergence) and ellipticity (shearing). Ensemble observations of the shear are collected and leveraged to construct estimates of the convergence, which can directly be related to the universal dark-matter distribution. Imminent stage IV surveys are forecast to accrue an unprecedented quantity of cosmological information; a discriminative partition of which is accessible through the convergence, and is disproportionately concentrated at high angular resolutions, where the echoes of cosmological evolution under gravity are most apparent. Capitalising on advances in probability concentration theory, this thesis merges the paradigms of Bayesian inference and optimisation to develop hybrid convergence inference techniques which are scalable, statistically principled, and operate over the Euclidean plane, celestial sphere, and 3-dimensional ball. Such techniques can quantify the plausibility of inferences at one-millionth the computational overhead of competing sampling methods. These Bayesian techniques are applied to the hotly debated Abell-520 merging cluster, concluding that observational catalogues contain insufficient information to determine the existence of dark-matter self-interactions. Further, these techniques were applied to all public lensing catalogues, recovering the then largest global dark-matter mass-map. The primary methodological contributions of this thesis depend only on posterior log-concavity, paving the way towards a, potentially revolutionary, complete hybridisation with artificial intelligence techniques. These next-generation techniques are the first to operate over the full 3-dimensional ball, laying the foundations for statistically principled universal dark-matter cartography, and the cosmological insights such advances may provide