Improving Time-Scale Modification of Music Signals Using Harmonic-Percussive Separation

A major problem in time-scale modification (TSM) of music signals is that percussive transients are often perceptually degraded. To prevent this degradation, some TSM approaches try to explicitly identify transients in the input signal and to handle them in a special way. However, such approaches are problematic for two reasons. First, errors in the transient detection have an immediate influence on the final TSM result and, second, a perceptual transparent preservation of transients is by far not a trivial task. In this paper we present a TSM approach that handles transients implicitly by first separating the signal into a harmonic component as well as a percussive component which typically contains the transients. While the harmonic component is modified with a phase vocoder approach using a large frame size, the noise-like percussive component is modified with a simple time-domain overlap-add technique using a short frame size, which preserves the transients to a hig h degree without any explicit transient detection

A novel method for transient detection in high-cadence optical surveys: Its application for a systematic search for novae in M31

[abridged] In large-scale time-domain surveys, the processing of data, from procurement up to the detection of sources, is generally automated. One of the main challenges is contamination by artifacts, especially in regions of strong unresolved emission. We present a novel method for identifying candidates for variables and transients from the outputs of such surveys' data pipelines. We use the method to systematically search for novae in iPTF observations of the bulge of M31. We demonstrate that most artifacts produced by the iPTF pipeline form a locally uniform background of false detections approximately obeying Poissonian statistics, whereas genuine variables and transients as well as artifacts associated with bright stars result in clusters of detections, whose spread is determined by the source localization accuracy. This makes the problem analogous to source detection on images produced by X-ray telescopes, enabling one to utilize tools developed in X-ray astronomy. In particular, we use a wavelet-based source detection algorithm from the Chandra data analysis package CIAO. Starting from ~2.5x10^5 raw detections made by the iPTF data pipeline, we obtain ~4000 unique source candidates. Cross-matching these candidates with the source-catalog of a deep reference image, we find counterparts for ~90% of them. These are either artifacts due to imperfect PSF matching or genuine variable sources. The remaining ~400 detections are transient sources. We identify novae among these candidates by applying selection cuts based on the expected properties of nova lightcurves. Thus, we recovered all 12 known novae registered during the time span of the survey and discovered three nova candidates. Our method is generic and can be applied for mining any target out of the artifacts in optical time-domain data. As it is fully automated, its incompleteness can be accurately computed and corrected for.Comment: 16 pages, 8 figures, accepted to A&

Data-driven Discovery of Transients in the New Era of Time-Domain Astronomy

Time-domain astronomy has reached an incredible new era where unprecedented amounts of data are becoming available. New large-scale astronomical surveys such as the Legacy Survey of Space and Time (LSST) are going to revolutionise transient astronomy, providing opportunities to discover entirely new classes of transients while also enabling a deeper understanding of known classes. LSST is expected to observe over 10 million transient alerts every night, at least two orders of magnitude more than any preceding survey. It has never been more important that astronomers develop fast and automated methods of identifying transient candidates for follow-up observations. In this thesis, I tackle two major challenges facing the future of transient astronomy: the early classification of transients and the detection of rare or previously unknown transients. I detail my development of a number of novel methods dealing with these issues. In the first chapter, I provide an introduction to the field of transient astronomy and motivate why new methods of transient identification are necessary. In the second chapter, I detail the development of a new photometric transient classifier, called RAPID, that is able to automatically classify a range of astronomical transients in real-time. My deep neural network architecture is the first method designed to provide early classifications of astronomical transients. In Chapter 3, I identify the issue that with such large data volumes, the astronomical community will struggle to identify rare and interesting anomalous transients that have previously been found serendipitously. I outline my novel method that uses a Bayesian parametric fit of light curves to identify anomalous transients in real-time. In Chapter 4, I highlight some issues with current photometric classifiers and improve upon RAPID so that it is capable of dealing with real data instead of just simulations. I present classifiers that perform effectively on real data from the Zwicky Transient Facility and the PanSTARRS surveys. Finally, in the last chapter, I discuss the conclusions of my work and highlight some future opportunities and work needed in preparing for discovery in the new era of time-domain astronomy.Cambridge Trust; Cambridge Australia Scholarship

Spike detection using the continuous wavelet transform

This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution

Classification methods for noise transients in advanced gravitational-wave detectors

Noise of non-astrophysical origin will contaminate science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and Advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and environmental noise transients will be critical for improving the sensitivity of the advanced detectors in the upcoming science runs. During the science runs of the initial gravitational-wave detectors, noise transients were manually classified by visually examining the time-frequency scan of each event. Here, we present three new algorithms designed for the automatic classification of noise transients in advanced detectors. Two of these algorithms are based on Principal Component Analysis. They are Principal Component Analysis for Transients (PCAT), and an adaptation of LALInference Burst (LIB). The third algorithm is a combination of an event generator called Wavelet Detection Filter (WDF) and machine learning techniques for classification. We test these algorithms on simulated data sets, and we show their ability to automatically classify transients by frequency, SNR and waveform morphology

Gravitational Waves and Time Domain Astronomy

The gravitational wave window onto the universe will open in roughly five years, when Advanced LIGO and Virgo achieve the first detections of high frequency gravitational waves, most likely coming from compact binary mergers. Electromagnetic follow-up of these triggers, using radio, optical, and high energy telescopes, promises exciting opportunities in multi-messenger time domain astronomy. In the next decade, space-based observations of low frequency gravitational waves from massive black hole mergers, and their electromagnetic counterparts, will open up further vistas for discovery. This two-part workshop at featured brief presentations and stimulating discussions on the challenges and opportunities presented by gravitational wave astronomy. Highlights from the workshop, with the emphasis on strategies for electromagnetic follow-up, are presented in this report.Comment: Submitted to Proc. IAU 285, "New Horizons in Transient Astronomy", Oxford, Sept. 201

A Phase Vocoder based on Nonstationary Gabor Frames

We propose a new algorithm for time stretching music signals based on the theory of nonstationary Gabor frames (NSGFs). The algorithm extends the techniques of the classical phase vocoder (PV) by incorporating adaptive time-frequency (TF) representations and adaptive phase locking. The adaptive TF representations imply good time resolution for the onsets of attack transients and good frequency resolution for the sinusoidal components. We estimate the phase values only at peak channels and the remaining phases are then locked to the values of the peaks in an adaptive manner. During attack transients we keep the stretch factor equal to one and we propose a new strategy for determining which channels are relevant for reinitializing the corresponding phase values. In contrast to previously published algorithms we use a non-uniform NSGF to obtain a low redundancy of the corresponding TF representation. We show that with just three times as many TF coefficients as signal samples, artifacts such as phasiness and transient smearing can be greatly reduced compared to the classical PV. The proposed algorithm is tested on both synthetic and real world signals and compared with state of the art algorithms in a reproducible manner.Comment: 10 pages, 6 figure